Vahid Taslimitehrani's Dissertation Defense: Friday, February 19 2015.
Ph.D. Committee: Drs. Guozhu Dong, Advisor, T.K. Prasad, Amit Sheth, Keke Chen
and Jyotishman Pathak, Division of Health Informatics, Weill Cornell Medical College, Cornell University.
ABSTRACT:
Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy.
In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where a given baseline model has large modeling errors, characterizing such regions using patterns, and learning specialized models for those regions. Each PXR/PXC model contains several pairs of contrast patterns and local models, where a local classifier is applied only to data instances matching its associated pattern. We also propose a class of classification and regression techniques called Contrast Pattern Aided Regression (CPXR) and Contrast Pattern Aided Classification (CPXC) to build accurate and interpretable PXR and PXC models.
We have conducted a set of comprehensive performance studies to evaluate the performance of CPXR and CPXC. The results show that CPXR and CPXC outperform state-of-the-art regression and classification algorithms, often by significant margins. The results also show that CPXR and CPXC are especially effective for heterogeneous and high dimensional datasets. Besides being new types of modeling, PXR and PXC models can also provide insights into data heterogeneity and diverse predictor-response relationships.
We have also adapted CPXC to handle classifying imbalanced datasets and introduced a new algorithm called Contrast Pattern Aided Classification for Imbalanced Datasets (CPXCim). In CPXCim, we applied a weighting method to boost minority instances as well as a new filtering method to prune patterns with imbalanced matching datasets.
Finally, we applied our techniques on three real applications, two in the healthcare domain and one in the soil mechanic domain. PXR and PXC models are significantly more accurate than other learning algorithms in those three applications.
Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for L...Amit Sheth
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010
Sujan Perera's Dissertation Defense: Friday, August 12, 2016
Ph.D. Committee: Drs. Amit Sheth, Advisor; T.K. Prasad, Michael Raymer, and Pablo Mendes (IBM Research)
Video: https://youtu.be/pbjJ1zb8ayY
ABSTRACT:
Natural language is a powerful tool developed by humans over hundreds of thousands of years. The extensive usage, flexibility of the language, creativity of the human beings, and social, cultural, and economic changes that have taken place in daily life have added new constructs, styles, and features to the language. One such feature of the language is its ability to express ideas, opinions, and facts in an implicit manner. This is a feature that is used extensively in day to day communications in situations such as: 1) expressing sarcasm, 2) when trying to recall forgotten things, 3) when required to convey descriptive information, 4) when emphasizing the features of an entity, and 5) when communicating a common understanding.
Consider the tweet 'New Sandra Bullock astronaut lost in space movie looks absolutely terrifying' and the text snippet extracted from a clinical narrative 'He is suffering from nausea and severe headaches. Dolasteron was prescribed.' The tweet has an implicit mention of the entity Gravity and the clinical text snippet has implicit mention of the relationship between medication Dolasteron and clinical condition nausea. Such implicit references of the entities and the relationships are common occurrences in daily communication and they add unique value to conversations. However, extracting implicit constructs has not received enough attention. This dissertation focuses on extracting implicit entities and relationships from clinical narratives and extracting implicit entities from Tweets.
This dissertation demonstrates manifestations of implicit constructs in text, studies their characteristics, and develops a solution that is capable of extracting implicit factual information from text. The developed solution starts by acquiring relevant knowledge to solve the implicit information extraction problem. The relevant knowledge includes domain knowledge, contextual knowledge, and linguistic knowledge. The acquired knowledge can take different syntactic forms such as a text snippet, structured knowledge represented in standard knowledge representation languages like Resource Description Framework (RDF) or custom formats. Hence, the acquired knowledge is processed to create models that can be understood by machines. Such models provide the infrastructure to perform implicit information extraction of interest.
This dissertation focuses on three different use cases of implicit information and demonstrates the applicability of the developed solution in these use cases. They are:
- implicit entity linking in clinical narratives,
- implicit entity linking in Twitter,
- implicit relationship extraction from clinical narratives.
Drug Repurposing using Deep Learning on Knowledge GraphsDatabricks
Discovering new drugs is a lengthy and expensive process. This means that finding new uses for existing drugs can help create new treatments in less time and with less time. The difficulty is in finding these potential new uses.
How do we find these undiscovered uses for existing drugs?
We can unify the available structured and unstructured data sets into a knowledge graph. This is done by fusing the structured data sets, and performing named entity extraction on the unstructured data sets. Once this is done, we can use deep learning techniques to predict latent relationships.
In this talk we will cover:
Building the knowledge graph
Predicting latent relationships
Using the latent relationships to repurpose existing drugs
2020.04.07 automated molecular design and the bradshaw platform webinarPistoia Alliance
This presentation described how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist. It explored what is reasonable to expect “AI” approaches might achieve, and what is best left with a human expert. The implications of automation for the human-machine interface were explored and illustrated with examples from Bradshaw, GSK’s experimental automated design environment.
Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for L...Amit Sheth
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010
Sujan Perera's Dissertation Defense: Friday, August 12, 2016
Ph.D. Committee: Drs. Amit Sheth, Advisor; T.K. Prasad, Michael Raymer, and Pablo Mendes (IBM Research)
Video: https://youtu.be/pbjJ1zb8ayY
ABSTRACT:
Natural language is a powerful tool developed by humans over hundreds of thousands of years. The extensive usage, flexibility of the language, creativity of the human beings, and social, cultural, and economic changes that have taken place in daily life have added new constructs, styles, and features to the language. One such feature of the language is its ability to express ideas, opinions, and facts in an implicit manner. This is a feature that is used extensively in day to day communications in situations such as: 1) expressing sarcasm, 2) when trying to recall forgotten things, 3) when required to convey descriptive information, 4) when emphasizing the features of an entity, and 5) when communicating a common understanding.
Consider the tweet 'New Sandra Bullock astronaut lost in space movie looks absolutely terrifying' and the text snippet extracted from a clinical narrative 'He is suffering from nausea and severe headaches. Dolasteron was prescribed.' The tweet has an implicit mention of the entity Gravity and the clinical text snippet has implicit mention of the relationship between medication Dolasteron and clinical condition nausea. Such implicit references of the entities and the relationships are common occurrences in daily communication and they add unique value to conversations. However, extracting implicit constructs has not received enough attention. This dissertation focuses on extracting implicit entities and relationships from clinical narratives and extracting implicit entities from Tweets.
This dissertation demonstrates manifestations of implicit constructs in text, studies their characteristics, and develops a solution that is capable of extracting implicit factual information from text. The developed solution starts by acquiring relevant knowledge to solve the implicit information extraction problem. The relevant knowledge includes domain knowledge, contextual knowledge, and linguistic knowledge. The acquired knowledge can take different syntactic forms such as a text snippet, structured knowledge represented in standard knowledge representation languages like Resource Description Framework (RDF) or custom formats. Hence, the acquired knowledge is processed to create models that can be understood by machines. Such models provide the infrastructure to perform implicit information extraction of interest.
This dissertation focuses on three different use cases of implicit information and demonstrates the applicability of the developed solution in these use cases. They are:
- implicit entity linking in clinical narratives,
- implicit entity linking in Twitter,
- implicit relationship extraction from clinical narratives.
Drug Repurposing using Deep Learning on Knowledge GraphsDatabricks
Discovering new drugs is a lengthy and expensive process. This means that finding new uses for existing drugs can help create new treatments in less time and with less time. The difficulty is in finding these potential new uses.
How do we find these undiscovered uses for existing drugs?
We can unify the available structured and unstructured data sets into a knowledge graph. This is done by fusing the structured data sets, and performing named entity extraction on the unstructured data sets. Once this is done, we can use deep learning techniques to predict latent relationships.
In this talk we will cover:
Building the knowledge graph
Predicting latent relationships
Using the latent relationships to repurpose existing drugs
2020.04.07 automated molecular design and the bradshaw platform webinarPistoia Alliance
This presentation described how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist. It explored what is reasonable to expect “AI” approaches might achieve, and what is best left with a human expert. The implications of automation for the human-machine interface were explored and illustrated with examples from Bradshaw, GSK’s experimental automated design environment.
Drug discovery and development is a long and expensive process over time has notoriously bucked Moore's law that it now has its own law called Eroom's Law named after it (the opposite of Moore). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of drug failures. Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible becomes all the more important to accelerate drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains. Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories: 1. Classification 2. Regression 3. Read-across. The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them. We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Semantic Web for Health Care and Biomedical InformaticsAmit Sheth
Amit Sheth, "Semantic Web for Health Care and Biomedical Informatics," Keynote at NSF Biomed Web Workshop, Corbett, Oregon, December 4-5, 2007.
http://www.biomedweb.info/2007/
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
A Semantic Retrieval System for Extracting Relationships from Biological Corpusijcsit
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This
paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean
model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the
researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
Data Provenance and Scientific Workflow ManagementNeuroMat
Introductory class on techniques and tools to manage scientific data, focusing on sources of information and data analysis. Lecturer: Prof. Kelly Rosa Braghetto, a NeuroMat associate investigator and a professor at the University of São Paulo's Department of Computer Science.
An efficient algorithm for sequence generation in data miningijcisjournal
Data mining is the method or the activity of analyzing data from different perspectives and summarizing it
into useful information. There are several major data mining techniques that have been developed and are
used in the data mining projects which include association, classification, clustering, sequential patterns,
prediction and decision tree. Among different tasks in data mining, sequential pattern mining is one of the
most important tasks. Sequential pattern mining involves the mining of the subsequences that appear
frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of
customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access
patterns, seismologic data and weather observations. Various models and algorithms have been developed
for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the
efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset
by applying various performance factors. From the experimental results, it is observed that the PrefixSpan
algorithm is more efficient than other two algorithms.
A Survey Ondecision Tree Learning Algorithms for Knowledge DiscoveryIJERA Editor
Theimmense volumes of data are populated into repositories from various applications. In order to find out desired information and knowledge from large datasets, the data mining techniques are very much helpful. Classification is one of the knowledge discovery techniques. In Classification, Decision trees are very popular in research community due to simplicity and easy comprehensibility. This paper presentsan updated review of recent developments in the field of decision trees.
Interlinking educational data to Web of Data (Thesis presentation)Enayat Rajabi
This is a thesis presentation about interlinking educational data to Web of Data. I explain how I used the Linked Data approach to expose and interlink educational data to the Linked Open Data cloud
Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award.
Cory Henson defended his thesis on "A Semantics-based Approach to Machine Perception".
Video can be found at: http://www.youtube.com/watch?v=L8M7eoGKtSE
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010
Drug discovery and development is a long and expensive process over time has notoriously bucked Moore's law that it now has its own law called Eroom's Law named after it (the opposite of Moore). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of drug failures. Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible becomes all the more important to accelerate drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains. Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories: 1. Classification 2. Regression 3. Read-across. The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them. We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Semantic Web for Health Care and Biomedical InformaticsAmit Sheth
Amit Sheth, "Semantic Web for Health Care and Biomedical Informatics," Keynote at NSF Biomed Web Workshop, Corbett, Oregon, December 4-5, 2007.
http://www.biomedweb.info/2007/
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
A Semantic Retrieval System for Extracting Relationships from Biological Corpusijcsit
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This
paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean
model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the
researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
Data Provenance and Scientific Workflow ManagementNeuroMat
Introductory class on techniques and tools to manage scientific data, focusing on sources of information and data analysis. Lecturer: Prof. Kelly Rosa Braghetto, a NeuroMat associate investigator and a professor at the University of São Paulo's Department of Computer Science.
An efficient algorithm for sequence generation in data miningijcisjournal
Data mining is the method or the activity of analyzing data from different perspectives and summarizing it
into useful information. There are several major data mining techniques that have been developed and are
used in the data mining projects which include association, classification, clustering, sequential patterns,
prediction and decision tree. Among different tasks in data mining, sequential pattern mining is one of the
most important tasks. Sequential pattern mining involves the mining of the subsequences that appear
frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of
customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access
patterns, seismologic data and weather observations. Various models and algorithms have been developed
for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the
efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset
by applying various performance factors. From the experimental results, it is observed that the PrefixSpan
algorithm is more efficient than other two algorithms.
A Survey Ondecision Tree Learning Algorithms for Knowledge DiscoveryIJERA Editor
Theimmense volumes of data are populated into repositories from various applications. In order to find out desired information and knowledge from large datasets, the data mining techniques are very much helpful. Classification is one of the knowledge discovery techniques. In Classification, Decision trees are very popular in research community due to simplicity and easy comprehensibility. This paper presentsan updated review of recent developments in the field of decision trees.
Interlinking educational data to Web of Data (Thesis presentation)Enayat Rajabi
This is a thesis presentation about interlinking educational data to Web of Data. I explain how I used the Linked Data approach to expose and interlink educational data to the Linked Open Data cloud
Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award.
Cory Henson defended his thesis on "A Semantics-based Approach to Machine Perception".
Video can be found at: http://www.youtube.com/watch?v=L8M7eoGKtSE
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010
Video: https://www.youtube.com/watch?v=ZCToaDgxnAs
Abstract:
People's emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse.
Emotions are both prevalent in and essential to most aspects of our lives. They influence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for identifying people's emotions expressed in text. It has valuable implications for the studies of suicide prevention, employee productivity, well-being of people, customer relationship management, etc. However, emotion identification is quite challenging partly due to the following reasons: i) It is a multi-class classification problem that usually involves at least six basic emotions. Text describing an event or situation that causes the emotion can be devoid of explicit emotion-bearing words, thus the distinction between different emotions can be very subtle, which makes it difficult to glean emotions purely by keywords. ii) Manual annotation of emotion data by human experts is very labor-intensive and error-prone. iii) Existing labeled emotion datasets are relatively small, which fails to provide a comprehensive coverage of emotion-triggering events and situations.
Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries.
First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases.
While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from 100+ million search queries from smarts devices (smartphones/tablets) and personal computers (desktops/laptops)
Video of the talk: https://www.youtube.com/watch?v=7k-u_TUew3o
Abstract: Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media.
Building a personalized filtering system involves understanding users interests and utilizing these interests to deliver relevant information to users. These tasks primarily include analyzing and processing social media text which is challenging due to its shortness in length, and the real-time nature of the medium. The challenges include: (1) Lack of semantic context: Social Media posts are on an average short in length, which provides limited semantic context to perform textual analysis. This is particularly detrimental for topic identification which is a necessary task for mining users interests; (2) Dynamically changing vocabulary: Most social media websites such as Twitter and Facebook generate posts that are of current (timely) interests to the users. Due to this real-time nature, information relevant to dynamic topics of interest evolve reflecting the changes in the real world. This in turn changes the vocabulary associated with these dynamic topics of interest making it harder to filter relevant information; (3) Scalability: The number of users on social media platforms are significantly large, which is difficult for centralized systems to scale to deliver relevant information to users. This dissertation is devoted to exploring semantic techniques and Semantic Web technologies to address the above mentioned challenges in building a personalized information filtering system for social media. Particularly, the necessary semantics (knowledge) is derived from crowd sourced knowledge bases such as Wikipedia to improve context for understanding short-text and dynamic topics on social media.
Dissertation Defense:
" Mining and Analyzing Subjective Experiences in User Generated Content "
By Lu Chen
Tuesday, April 9, 2016
Dissertation Committee: Dr. Amit Sheth, Advisor, Dr. T. K. Prasad, Dr. Keke Chen, Dr. Ingmar Weber, and Dr. Justin Martineau,
Pictures: https://www.facebook.com/Kno.e.sis/photos/?tab=album&album_id=1225911137443732
Video: https://youtu.be/tzLEUB-hggQ
Lu's Home page: http://knoesis.wright.edu/researchers/luchen/
ABSTRACT
Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information -- the information about people's subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in ever-growing user generated data on social media platforms, it is still difficult to extract high quality subjective information and make full use of it with current technologies.
Current subjectivity and sentiment analysis research has largely focused on classifying the text polarity -- whether the expressed opinion regarding a specific topic in a given text is positive, negative, or neutral. This narrow definition does not take into account the other types of subjective information such as emotion, intent, and preference, which may prevent their exploitation from reaching its full potential. This dissertation extends the definition and introduces a unified framework for mining and analyzing diverse types of subjective information. We have identified four components of a subjective experience: an individual who holds it, a target that elicits it (e.g., a movie, or an event), a set of expressions that describe it (e.g., "excellent", "exciting"), and a classification or assessment that characterize it (e.g., positive vs. negative). Accordingly, this dissertation makes contributions in developing novel and general techniques for the tasks of identifying and extracting these components.
We first explore the task of extracting sentiment expressions from social media posts. We propose an optimization-based approach that extracts a diverse set of sentiment-bearing expressions, including formal and slang words/phrases, for a given target from an unlabeled corpus. Instead of associating the overall sentiment with a given text, this method assesses the more fine-grained target-dependent polarity of each sentiment expression. Unlike pattern-based approaches which often fail to capture the diversity of sentiment expressions due to the informal nature of language usage and writing style in social media posts, the proposed approach is capable of identifying sentiment phrase
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
Description - Ajith defended his thesis on application and data portability in cloud
computing. More details on Ajith's research and publications can be
found at http://knoesis.wright.edu/researchers/ajith/
Video can be found at : http://www.youtube.com/watch?v=oDBeBIIFmHc&list=UUORqXk1ZV44MOwpCorAROyQ&index=1&feature=plpp_video
The recent emergence of the “Linked Data” approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" – i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 70 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud – as we will illustrate – are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short.
This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy.
In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where a given baseline model has large modeling errors, characterizing such regions using patterns, and learning specialized models for those regions. Each PXR/PXC model contains several pairs of contrast patterns and local models, where a local classifier is applied only to data instances matching its associated pattern. We also propose a class of classification and regression techniques called Contrast Pattern Aided Regression (CPXR) and Contrast Pattern Aided Classification (CPXC) to build accurate and interpretable PXR and PXC models.
We have conducted a set of comprehensive performance studies to evaluate the performance of CPXR and CPXC. The results show that CPXR and CPXC outperform state-of-the-art regression and classification algorithms, often by significant margins. The results also show that CPXR and CPXC are especially effective for heterogeneous and high dimensional datasets. Besides being new types of modeling, PXR and PXC models can also provide insights into data heterogeneity and diverse predictor-response relationships.
We have also adapted CPXC to handle classifying imbalanced datasets and introduced a new algorithm called Contrast Pattern Aided Classification for Imbalanced Datasets (CPXCim). In CPXCim, we applied a weighting method to boost minority instances as well as a new filtering method to prune patterns with imbalanced matching datasets.
Finally, we applied our techniques on three real applications, two in the healthcare domain and one in the soil mechanic domain. PXR and PXC models are significantly more accurate than other learning algorithms in those three applications.
A new CPXR Based Logistic Regression Method and Clinical Prognostic Modeling ...Vahid Taslimitehrani
Presented at 15th International Conference on BioInformatics and BioEngineering (BIBE2014)
Prognostic modeling is central to medicine, as it is often used to predict patients’ outcome and response to treatments and to identify important medical risk factors. Logistic regression is one of the most used approaches for clinical pre- diction modeling. Traumatic brain injury (TBI) is an important public health issue and a leading cause of death and disability worldwide. In this study, we adapt CPXR (Contrast Pattern Aided Regression, a recently introduced regression method), to develop a new logistic regression method called CPXR(Log), for general binary outcome prediction (including prognostic modeling), and we use the method to carry out prognostic modeling for TBI using admission time data. The models produced by CPXR(Log) achieved AUC as high as 0.93 and specificity as high as 0.97, much better than those reported by previous studies. Our method produced interpretable prediction models for diverse patient groups for TBI, which show that different kinds of patients should be evaluated differently for TBI outcome prediction and the odds ratios of some predictor variables differ significantly from those given by previous studies; such results can be valuable to physicians.
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Exact Data Reduction for Big Data by Jieping YeBigMine
Recent technological innovations have enabled data collection of unprecedented size and complexity. Examples include web text data, social media data, gene expression images, neuroimages, and genome-wide association study (GWAS) data. Such data have incredible potential to address complex scientific and societal questions, however analysis of these data poses major challenges for the scientists. As an emerging and powerful tool for analyzing massive collections of data, data reduction in terms of the number of variables and/or the number of samples has attracted tremendous attentions in the past few years, and has achieved great success in a broad range of applications. The intuition of data reduction is based on the observation that many real-world data with complex structures and billions of variables and/or samples can usually be well explained by a few most relevant explanatory features and/or samples. Most existing methods for data reduction are based on sampling or random projection, and the final model based on the reduced data is an approximation of the true (original) model. In this talk, I will present fundamentally different approaches for data reduction in that there is no approximation in the model, that is, the final model constructed from the reduced data is identical to the original model constructed from the complete data. Finally, I will use several real world examples to demonstrate the potential of exact data reduction for analyzing big data.
With R, Python, Apache Spark and a plethora of other open source tools, anyone with a computer can run machine learning algorithms in a jiffy! However, without an understanding of which algorithms to choose and when to apply a particular technique, most machine learning efforts turn into trial and error experiments with conclusions like "The algorithms don't work" or "Perhaps we should get more data".
In this lecture, we will focus on the key tenets of machine learning algorithms and how to choose an algorithm for a particular purpose. Rather than just showing how to run experiments in R ,Python or Apache Spark, we will provide an intuitive introduction to machine learning with just enough mathematics and basic statistics.
We will address:
• How do you differentiate Clustering, Classification and Prediction algorithms?
• What are the key steps in running a machine learning algorithm?
• How do you choose an algorithm for a specific goal?
• Where does exploratory data analysis and feature engineering fit into the picture?
• Once you run an algorithm, how do you evaluate the performance of an algorithm?
Basics of Data Analysis in BioinformaticsElena Sügis
Presentation gives introduction to the Basics of Data Analysis in Bioinformatics.
The following topics are covered:
Data acquisition
Data summary(selecting the needed column/rows from the file and showing basic descriptive statistics)
Preprocessing (missing values imputation, data normalization, etc.)
Principal Component Analysis
Data Clustering and cluster annotation (k-means, hierarchical)
Cluster annotations
Introduction to 16S rRNA gene multivariate analysisJosh Neufeld
Short introductory talk on multivariate statistics for 16S rRNA gene analysis given at the 2nd Soil Metagenomics conference in Braunschweig Germany, December 2013. A previous talk had discussed quality filtering, chimera detection, and clustering algorithms.
SVM-PSO based Feature Selection for Improving Medical Diagnosis Reliability u...cscpconf
Improving accuracy of supervised classification algorithms in biomedical applications,
especially CADx, is one of active area of research. This paper proposes construction of rotation
forest (RF) ensemble using 20 learners over two clinical datasets namely lymphography and
backache. We propose a new feature selection strategy based on support vector machines
optimized by particle swarm optimization for relevant and minimum feature subset for obtaining
higher accuracy of ensembles. We have quantitatively analyzed 20 base learners over two
datasets and carried out the experiments with 10 fold cross validation leave-one-out strategy
and the performance of 20 classifiers are evaluated using performance metrics namely accuracy
(acc), kappa value (K), root mean square error (RMSE) and area under receiver operating
characteristics curve (ROC). Base classifiers succeeded 79.96% & 81.71% average accuracies
for lymphography & backache datasets respectively. As for RF ensembles, they produced
average accuracies of 83.72% & 85.77% for respective diseases. The paper presents promising
results using RF ensembles and provides a new direction towards construction of reliable and robust medical diagnosis systems.
Poster for Society for Clinical Trials annual meeting in Boston, MA
Abstract
Randomization methods generally are designed to be both unpredictable and balanced between treatment allocations overall and within strata. However, when planning studies, little consideration is given to measuring these characteristics, nor are they examined jointly, and published comparisons between methods often use incompatible metrics and simulation assumptions. Furthermore, for purposes of real-world planning, such simulations often make unrealistic assumptions (e.g., equal sized strata), and summary statistics give limited information.
Similar to Contrast Pattern Aided Regression and Classification (20)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Contrast Pattern Aided Regression and Classification
1. Ohio Center of Excellence in Knowledge-Enabled Computing
Ph.D. Dissertation Defense:
Contrast Pattern Aided Regression and
Classification
February 19, 2016
Vahid Taslimitehrani
Kno.e.sis Center, CSE Dept., Wright State University, USA
Committee Members: Prof. Guozhu Dong (advisor, WSU), Prof. Amit Sheth (WSU),
Prof. T.K. Prasad (WSU), Dr. Keke Chen (WSU), and Prof. Jyotishman Pathak
(Cornell University)
1
2. Ohio Center of Excellence in Knowledge-Enabled Computing
2
3. Ohio Center of Excellence in Knowledge-Enabled Computing
3
Does Asthma decrease
the mortality risk from
Pneumonia?
4. Ohio Center of Excellence in Knowledge-Enabled Computing
Accuracy vs. Interpretability
4
Accuracy
Interpretability
Low
High
High
Lasso
Linear/Logistic
Regression
Naïve Bayes
Decision Trees
Splines
Nearest
Neighbors
Bagging
Neural Nets
SVM
Boosting
Random Forest
Deep Learning
CPXR/CPXC
Source: Joshua Bloom and Henrik Brink of wise.io
*on real dataset
5. Ohio Center of Excellence in Knowledge-Enabled Computing
5
Modeling Techniques Lack Accuracy
and Interpretability
Heterogeneity &
Diversity of Given
Dataset
Predictors-Response
Interactions
Universal Model’s
Assumption
6. Ohio Center of Excellence in Knowledge-Enabled Computing
Predictors-Response Interactions
6
Interactive effect:
The effect of a variable on prediction
changes and varies with changes in the
values of other independent variable(s)
which are interacting with the variable.
It is not the genes or the environment!
It is their interaction that’s important.
7. Ohio Center of Excellence in Knowledge-Enabled Computing
Universal Model’s Assumption &
Heterogeneity
What is the universal model’s
assumption?
7
What are heterogeneous and
diverse data points?
8. Ohio Center of Excellence in Knowledge-Enabled Computing
Solution
1.New type of regression & classification models called Pattern
Aided Regression and Classification (PXR and PXC)
2.The new algorithms to build PXR and PXC models called Contrast
Pattern Aided Regression and Classification (CPXR and CPXC)
3.The new algorithm to handle imbalanced datasets called Contrast
Pattern Aided Classification on Imbalanced datasets (CPXCim)
8
Our proposed methodology has three components:
9. Ohio Center of Excellence in Knowledge-Enabled Computing
Preliminaries: patterns
• A pattern (rule) is a set of conditions describing set of objects.
• Example:
"𝑨𝒈𝒆 ≥ 60" AND “History of hypertension = YES”
is a pattern (rule) describing:
All patients more than 60 years old AND have a history of Hypertension.
• An object matches a pattern if it satisfies every condition in the pattern.
9
Patient ID Age BMI History of Hypertension Diagnosed with Heart Failure
1 75 22 YES YES
2 67 27 NO NO
10. Ohio Center of Excellence in Knowledge-Enabled Computing
Preliminaries: matching dataset and
contrast patterns
• The matching dataset of pattern 𝑃 in dataset 𝐷 or 𝑚𝑑𝑠(𝑃, 𝐷) is the set of all
instances matching pattern 𝑃.
• The support of pattern 𝑃 in 𝐷 is 𝑠𝑢𝑝𝑝 𝑃, 𝐷 =
𝑚𝑑𝑠(𝑃,𝐷)
𝐷
.
• Contrast patterns: patterns that distinguish objects in different classes. A
pattern is contrast pattern if it matches many objects in one class than in
another class.
• An equivalent class (EC) is a set of patterns with same matching datasets
(having same behavior).
10
11. Ohio Center of Excellence in Knowledge-Enabled Computing
Introduction: CPXR/CPXC overview
11
𝑷: pattern
𝒇: model
A pattern logically
characterizes a sub-
group of data.
A local model represents
predictor-response
interactions among the
data points of a sub-
group of data.
Regression
Classification
𝒇
CPXR/CPXC
(𝑷 𝟏, 𝒇 𝟏)
(𝑷 𝟐, 𝒇 𝟐)
Local model algorithms
can be simple as linear
regression.
12. Ohio Center of Excellence in Knowledge-Enabled Computing
Diversity of predictor-response
relationships
• Different pattern-model pairs emphasize different sets of
variables.
• Different pattern-model pairs use highly different
regression/classification models.
• Diverse predictor-response relationships may be neutralized
at the global level.
12
13. Ohio Center of Excellence in Knowledge-Enabled Computing
Introduction: Thesis Statement
Study regression and classification techniques to produce accurate
and interpretable models capable of adequately representing
complex and diverse predictor-response interactions and revealing
high intra-dataset heterogeneity.
13
14. Ohio Center of Excellence in Knowledge-Enabled Computing
Contrast Pattern Aided Regression
(CPXR)
14
Guozhu Dong, Vahid Taslimitehrani, Pattern-Aided Regression
Modeling and Prediction Model Analysis. in IEEE Transactions
on Knowledge and Data Engineering, vol.27, no.9, pp.2452-
2465, Sept. 1 2015
15. Ohio Center of Excellence in Knowledge-Enabled Computing
A pictorial illustration of a simple PXR
model
15
A small dataset with 100 instances and 2 numerical
predictor variables.
• Different patterns can involve different sets of variables
[describing data regions in different subspaces]
• Matching datasets of different patterns can overlap
0
2
4
6
8
10
0 2 4 6 8 10
16. Ohio Center of Excellence in Knowledge-Enabled Computing
PXR concepts
16
Regression
Classification
𝒇 𝒃
Given a training dataset 𝐷 =
(𝑥𝑖, 𝑦𝑖) 1 ≤ 𝑖 ≤ 𝑛 , a regression
model built on 𝐷 is called
baseline model and given as 𝑓𝑏.
(𝑷 𝟏, 𝒇 𝑷 𝟏
)
(𝑷 𝟐, 𝒇 𝑷 𝟐
)
CPXR/CPXC
Given the matching dataset
of pattern 𝑃, 𝑚𝑑𝑠(𝑃, 𝐷), a
regression built on
𝑚𝑑𝑠 𝑃, 𝐷 is called local
model and is shown by 𝑓𝑃.
17. Ohio Center of Excellence in Knowledge-Enabled Computing
Pattern Items Local Model Match
𝑃1 𝑓1
𝑃2 𝑓2
𝑃3 𝑓3
𝑃4 𝑓4
𝑃5 𝑓5
𝑃6 𝑓6
Pattern Aided Regression (PXR)
17
• 𝑃𝑋𝑅 = ( 𝑃1, 𝑓1, 𝑤1 , 𝑃2, 𝑓2, 𝑤2 , … , 𝑃𝑘, 𝑓𝑘, 𝑤 𝑘 , 𝑓𝑑)
• The regression function of PXR as:
𝑓𝑃𝑋𝑅 =
𝑃 𝑖∈𝜋 𝑥
𝑤𝑖 𝑓𝑖(𝑥)
𝑃 𝑖∈𝜋 𝑥
𝑤𝑖
, 𝑖𝑓 𝜋 𝑥 ≠ ∅
𝑓𝑑, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
where 𝜋 𝑥 = 𝑃𝑖 1 ≤ 𝑖 ≤ 𝑘, 𝑥 𝑚𝑎𝑡𝑐ℎ𝑒𝑠 𝑃𝑖
Case 3:
Case 2:
Case 1:
18. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXR/CPXC: Quality Measures
• The average residual reduction (arr) of a pattern 𝑃 w.r.t to a prediction
model 𝑓 on a dataset 𝐷 is:
𝑎𝑟𝑟 𝑃 =
𝑥∈𝑚𝑑𝑠(𝑃,𝐷) 𝑟 𝑥(𝑓 𝑏) − 𝑥∈𝑚𝑑𝑠(𝑃,𝐷) 𝑟 𝑥(𝑓 𝑃)
𝑚𝑑𝑠(𝑃,𝐷)
• The total residual reduction (trr) of a PXR/PXC is:
𝑡𝑟𝑟 𝑃𝑋𝑅/𝑃𝑋𝐶 =
𝑥∈𝑚𝑑𝑠(𝑃𝑆,𝐷) 𝑟𝑥(𝑓𝑏) − 𝑥∈𝑚𝑑𝑠(𝑃𝑆,𝐷) 𝑟𝑥(𝑓𝑃𝑋𝑅/𝑃𝑋𝐶)
𝑥∈𝐷 𝑟𝑥(𝑓)
Where 𝑃𝑆 = 𝑃1, … , 𝑃𝑘 is the pattern set, 𝑟𝑥(𝑓) is the 𝑓’s residual on an
instance 𝑥 and 𝑚𝑑𝑠 𝑃𝑆, 𝐷 = 𝑖=1
𝑘
𝑚𝑑𝑠(𝑃𝑖, 𝐷).
18
19. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXR Algorithm
19
Dataset D CPXR
Phase1
Phase2
Phase3
Goal: A small set of cooperating patterns, where each pattern
characterize a subgroup of data points.
• A baseline model makes large residual errors on data points in
the subgroup.
• A highly accurate model is found to correct those errors.
20. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXR Algorithm
20
Baseline
model
Regression/
Classification
LE
SE
Training
Dataset
𝑃2
𝑃3
…
…
(𝑓2, 𝑤2)
(𝑓3, 𝑤3)
…
…
Patterns Local Models
Pattern
Mining
[(𝑃1, 𝑓1, 𝑤1) , (𝑃4, 𝑓4, 𝑤4) , … , (𝑃𝑘, 𝑓𝑘, 𝑤 𝑘)]
(𝑓1, 𝑤1)
(𝑓4, 𝑤4)
(𝑓𝑘, 𝑤 𝑘)
𝑃1
𝑃4
𝑃𝑘
21. Ohio Center of Excellence in Knowledge-Enabled Computing
• How to determine spliting point 𝜅?
Minimize 𝜌 −
𝑟 𝑖>𝜅 𝑟 𝑖
𝑟 𝑖
• How to select patterns from C𝑃𝑆?
Lets 𝑃𝑆 = 𝑃0 , where 𝑃0 is the pattern 𝑃 in C𝑃𝑆 with the highest 𝑎𝑟𝑟
21
0
1
2
3
4
5
6
0 50 100 150 200
SE LE
CPXR Algorithm
22. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXR/CPXC: Filtering methods
• Contrast patterns of LE with support ratio less than 1.
• Patterns with tiny residual reduction (𝑎𝑟𝑟).
• Patterns with Jaccard similarity more than 0.9
𝐽 𝑃1, 𝑃2 =
𝑚𝑑𝑠(𝑃1, 𝐷) ∩ 𝑚𝑑𝑠(𝑃2, 𝐷)
𝑚𝑑𝑠(𝑃1, 𝐷) ∪ 𝑚𝑑𝑠(𝑃2, 𝐷)
• Patterns with the size of matching datasets less than the number of
predictor variables.
22
23. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXR: Prediction Accuracy Evaluation
• 50 real datasets and 23 synthetic datasets
• Different criteria to generate synthetic datasets
• Compare CPXR’s performance with 5 state-of-the-art
regression methods
• Overfitting and noise sensitivity
• Analysis of parameters
23
𝑅𝑀𝑆𝐸 𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 =
𝑅𝑀𝑆𝐸 𝐿𝑅 − 𝐸𝑀𝑆𝐸(𝑋)
𝑅𝑀𝑆𝐸(𝐿𝑅)
24. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXR: Prediction Accuracy Evaluation
24
Dataset PLR SVR BART GBM CPXR
Tecator 40.62 0.16 19.35 -14.15 65.1
Tree 17.68 7.92 -7.23 -10.82 61.73
Wage 12.2 9.15 25.42 11.86 38.45
Average 18.41 4.94 20.18 14.6 42.89
CPXR’s
performance
vs. other
methods
• CPXR has the highest accuracy in 41 out of 50 datasets.
• CPXR’s results are more accurate than LR in all 50 datasets.
• In 20% of datasets, CPXR achieved more than 60% RMSE
reduction.
25. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXR: Overfitting and Noise Sensitivity
25
5 10 15 20
102030405060
Noise(%)
Dropinaccuracycomparingtocleantestdata(%)
●
●
●
●
●
Datasets
BART
CPXR
Gradient Boosting
NN SVR BART CPXR
0.00.20.40.6
NN SVR BART CPXR
−0.2−0.10.00.10.20.30.4
RMSE
reduction on
synthetic
datasets
Train - Test
Method Training Test
Drop in
accuracy
PLR 37.11% 18.76% 49%
SVR 7.65% 4.8% 37%
BART 41.02% 20.15% 51%
CPXR(LL) 51.4% 39.88% 22%
CPXR(LP) 53.85% 42.89% 21%
26. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXR: Analysis of Parameters
26
5 10 15 20
0.350.400.450.500.550.600.65
k (Number of patterns)
RMSEimprovementoverLR
●
●
●
●
●
●
Datasets
Fat
Mussels
Price
0.02 0.04 0.06 0.08 0.10
0.250.300.350.400.450.500.550.60
minSup
RMSEimprovementoverLR
● ●
●
●
●
Datasets
Fat
Mussels
Price
0.40 0.45 0.50 0.55 0.60 0.65 0.70
0.350.400.450.500.550.60
r
RMSEimprovementoverLR
● ●
●
● ●
● ●
●
Datasets
Fat
Mussels
Price
2% is the optimal minSup.7 patterns as average on
50 datasets.
27. Ohio Center of Excellence in Knowledge-Enabled Computing
Contrast Pattern Aided Classification
(CPXC)
27
Guozhu Dong, Vahid Taslimitehrani, Pattern Aided
Classification, SIAM Data Mining Conference, 2016
28. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXC: PXC Concept
CPXC techniques are quite
similar to those of CPXR
but CPXC has more
challenges as well as more
opportunities than CPXR
28
CPXC
Confidence
of Match
Objective
Functions
Classification
Algorithms
Loss
Functions
29. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXC: Confidence of Match
• Given 𝑃𝑋𝐶 = ( 𝑃1, ℎ 𝑃1
, 𝑤1 , 𝑃2, ℎ 𝑃2
, 𝑤2 , … , 𝑃𝑘, ℎ 𝑃 𝑘
, 𝑤 𝑘 , ℎ 𝑑), the class variable
of an instance 𝑥 is defined as:
𝑤𝑒𝑖𝑔ℎ𝑡𝑑 − 𝑣𝑜𝑡𝑒 (𝑃𝑋𝐶, 𝐶𝑗, 𝑥)
=
𝑃 𝑖∈𝜋 𝑥
𝑤𝑖 × 𝑚𝑎𝑡𝑐ℎ (𝑥, 𝑝𝑖) × ℎ 𝑝 𝑖
(𝑥, 𝐶𝑗)
𝑃 𝑖∈𝜋 𝑥
𝑤𝑖 × 𝑚𝑎𝑡𝑐ℎ (𝑥, 𝑝𝑖)
, 𝑖𝑓 𝜋 𝑥 ≠ ∅
ℎ 𝑑, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
where 𝜋 𝑥 = 𝑃𝑖 1 ≤ 𝑖 ≤ 𝑘, 𝑚𝑎𝑡𝑐ℎ 𝑥, 𝑝𝑖 > 0
and
𝑚𝑎𝑡𝑐ℎ 𝑥, 𝑝𝑖 =
𝑞 𝑖 𝜖𝑀𝐺(𝑝 𝑖) 𝑡 𝑚𝑎𝑡𝑐ℎ𝑒𝑠 𝑝 𝑖
𝑀𝐺(𝑝 𝑖)
• 𝑚𝑎𝑡𝑐ℎ 𝑥, 𝑝𝑖 is the fraction of 𝑀𝐺 ‘s 𝑞 in 𝑀𝐺 𝑝𝑖 such that 𝑥 matches 𝑞.
• ℎ 𝑝(𝑥, 𝐶𝑗) is the confidence score of local model ℎ on instance 𝑥 for class 𝐶𝑗.
29
Confidence
of Match
30. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXC: Loss Functions
30
0.600.650.700.750.800.850.90
ClassError
AUC
●
●
●
Binary Probabilistic Standardized
●
Datasets
ILPD
Hillvalley
Planning
Probabilistic error loss
function returns the
best results.
Loss
Functions
31. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXC: Base/Local Algorithms & Objective
Functions
• Different methods for baseline and local classifiers:
– We used 6 classification algorithm for learning the
baseline and local classifiers
31
Classification
Algorithms
• Quality measures on pattern sets
– We used 𝑡𝑟𝑟, AUC, and ACC (accuracy) to measure the
quality of a pattern set
• Quality measures on patterns and weights on local classifiers
– We used 𝑎𝑟𝑟, AUC, and ACC (accuracy) to measure the
quality of a pattern: 𝑎𝑟𝑟 is the winner!
Objective
Functions
32. Ohio Center of Excellence in Knowledge-Enabled Computing
Experimental results
32
19
Public
Datasets
8
Classification
Algorithms
Noise
Sensitivity &
Overfitting
Running
Time
7
Fold Cross
Validation
minSup = 0.02
rho = 0.45
33. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXC: Performance
Dataset Boosting DT NBC Log RF SVM Max CPXC (NBC-DT)
Congress 0.58 0.66 0.6 0.57 0.58 0.58 0.66 0.86
Poker 0.6 0.6 0.5 0.5 0.76 0.5 0.76 0.85
HillValley 0.5 0.63 0.65 0.66 0.6 0.67 0.67 0.89
Climate 0.96 0.81 0.9 0.94 0.97 0.98 0.98 0.97
Mammography 0.94 0.91 0.94 0.94 0.93 0.93 0.94 0.98
Steel 0.96 0.88 0.91 0.95 0.95 0.94 0.95 0.99
33
• CPXC achieved average AUC of 0.886 on the 8 hard datasets.
• Average AUC of the best performing traditional classifier (RF) on hard datasets is 0.638.
• CPXC’s AUC is never lower than RF on the hard datasets.
• CPXC achieved average AUC of 0.983 on the easy datasets while the best performing
traditional algorithms obtained average AUC of 0.968.
35. Ohio Center of Excellence in Knowledge-Enabled Computing
CPXC: Impact of Parameters
35
4 6 8 10 12 14
0.750.800.850.90
k (Number of patterns)
AUC
●
●
●
●
● ●
●
Datasets
Blood
Congress
Hillvalley
Planning
0.02 0.04 0.06 0.08 0.10
0.700.750.800.850.90
minSup
AUC
●
●
●
●
●
Datasets
Blood
Congress
Hillvalley
Planning
0.840.850.860.870.880.890.90
Objective Function
AUC
●
●
●
TER AUC ACC
●
Datasets
ILPD
Hillvalley
Planning
0.3 0.4 0.5 0.6 0.7
0.780.800.820.840.860.880.90
r
AUC
●
●
●
● ●
●
●
●
●
●
Datasets
Blood
Congress
Hillvalley
Planning
36. Ohio Center of Excellence in Knowledge-Enabled Computing
36
Classification on Imbalanced Datasets
• What is an imbalanced classification problem?
• What are the real world applications?
• Why traditional classification algorithms do not perform well on
imbalanced datasets?
• What is our proposed solution?
Classifying minority instances might be more important that majority class.
37. Ohio Center of Excellence in Knowledge-Enabled Computing
LE
SE
37
Baseline
model
Classification
LE
SE
Training
Dataset
Weighting
• 𝑒𝑟𝑟∗ ℎ 𝑏, 𝑥 =
𝑒𝑟𝑟 ℎ 𝑏, 𝑥 × 𝛿, 𝑖𝑓𝑥 ∈ 𝑚𝑖𝑛𝑜𝑟𝑖𝑡𝑦 𝑐𝑙𝑎𝑠𝑠 𝑖𝑛𝑠𝑡𝑎𝑛𝑥𝑐𝑒𝑠
𝑒𝑟𝑟(ℎ 𝑏, 𝑥), 𝑖𝑓𝑥 ∈ 𝑚𝑎𝑗𝑜𝑟𝑖𝑡𝑦 𝑐𝑙𝑎𝑠𝑠 𝑖𝑛𝑠𝑡𝑎𝑛𝑥𝑐𝑒𝑠
New Weighting idea
38. Ohio Center of Excellence in Knowledge-Enabled Computing
A Filtering Method to Remove Imbalanced
Local Models
38
• 𝐼𝑅 𝑚𝑑𝑠 𝑃, 𝐷 =
Number of instances in the majority class
Number of instances in the minority class
𝑃1
𝑃2
𝑃3
𝑃4
…
…
𝑃𝑘
(𝑓1, 𝑤1)
(𝑓2, 𝑤2)
(𝑓3, 𝑤3)
(𝑓4, 𝑤4)
…
…
(𝑓𝑘, 𝑤 𝑘)
Patterns Local Models
39. Ohio Center of Excellence in Knowledge-Enabled Computing
Experimental results
39
• The average AUC of CPXCim is 14% and 15.2% more than the AUC of
SMOTE and SMOTE-TL, respectively.
• The performance of CPXCim is always better than other imbalanced
classifiers on these 10 datasets.
CPXCim’s performance
Dataset
# of
instances
# of
variables
Imbalance
ratio
CPXCim SMOTE SMOTE-TL
Yeast 1004 8 9.14 0.942 0.7728 0.772
Led7digit 443 7 10.97 0.978 0.8919 0.897
flareF 1066 11 23.79 0.883 0.7463 0.809
Wine Quality 1599 11 29.17 0.76 0.6008 0.59
Average - - - 0.92 0.798 0.807
40. Ohio Center of Excellence in Knowledge-Enabled Computing
Applications of CPXR & CPXC
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• Vahid Taslimitehrani, Guozhu Dong. A New CPXR Based Logistic Regression Method and Clinical
Prognostic Modeling Results Using the Method on Traumatic Brain Injury", IEEE International
Conference on Bioinformatics and Bioengineering (BIBE), 2014, On page(s): 283 – 290 (Best Student
Paper)
• Behzad Ghanbarian, Vahid Taslimitehrani, Guozhu Dong, Yakov Pachepsky. Sample dimensions
effect on prediction of soil water retention curve and saturated hydraulic conductivity. Journal of
Hydrology. 528 (2015): 127-137.
• Vahid Taslimitehrani, Guozhu Dong, Naveen Pereira, Maryam Panahiazar, Jyotishman Pathak.
Develolping HER-driven Heart Failure Models using CPXR(Log) with the probabilistic loss function.
Journal of Biomedical Informatics (2016).
41. Ohio Center of Excellence in Knowledge-Enabled Computing
Application: Traumatic Brain Injury
What is Traumatic Brain Injury (TBI)?
It is an important public health problem and a leading
cause of death and disability worldwide.
Problem definition: prediction of patients outcome
within 6 months after TBI event, using the admission data.
• Dataset: 2159 patients collected from a trial and 15 predictor variables
• Two class variables: mortality and unfavorable outcome.
41
Vahid Taslimitehrani, Guozhu Dong. A New CPXR Based Logistic Regression
Method and Clinical Prognostic Modeling Results Using the Method on
Traumatic Brain Injury", Bioinformatics and Bioengineering (BIBE), 2014
IEEE International Conference on, On page(s): 283 – 290 (Best Student
Paper Award)
43. Ohio Center of Excellence in Knowledge-Enabled Computing
Application: Heart Failure Survival Risk
Models
• Collaboration with Mayo Clinic
• Problem definition: Heart Failure survival prediction models.
• An EHR dataset on 119,749 patients admitted to Mayo Clinic.
• Predictor variables are grouped in the following categories:
– Demographic, Vitals, Labs, Medications and 24 major chronic conditions as co-
morbidities.
• Three groups of CPXC models are developed to predict survival in 1, 2 and 5 years
after heart failure event.
43
Vahid Taslimitehrani, Guozhu Dong, Naveen Pereira, Maryam Panahiazar, Jyotishman Pathak.
Develolping HER-driven Heart Failure Models using CPXR(Log) with the probabilistic loss function.
Journal of Biomedical Informatics (2016).
44. Ohio Center of Excellence in Knowledge-Enabled Computing
Application: Heart Failure Survival Risk
Models
Algorithm 1 Year 2 Year 5 Year
Decision Tree 0.66 0.5 0.5
Random Forest 0.8 0.72 0.72
Ada Boost 0.74 0.71 0.68
SVM 0.59 0.52 0.52
Logistic Regression 0.81 0.74 0.73
CPXC 0.937 0.83 0.786
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Variable Log f1 f2 f3 f4 f5 f6 f7
Alzheimer 1.75 1.74 0.80 1.88 1.59 1.29 1.58 0.75
Breast Cancer 0.63 1.15 1.62 2.73 1.00 1.00 2.08 0.59
Odds ratios of PXC local models
Performance of difference classifiers
45. Ohio Center of Excellence in Knowledge-Enabled Computing
Application: Heart Failure Survival Risk
Models
Variable sets CPXC Log RF SVM DT Boosting
(Demo&Vital) (Demo&Vital) +Lab 4.8% 11.5% 19% 17.3% 0% 14.7%
(Demo&Vital) (Demo&Vital) +Lab+Med 8.9% 13.4% 21.2% 21.7% 0% 5.7%
(Demo&Vital) (Demo&Vital) +Lab+Med+Co-morbid 27.8% 9.6% 19.1% 19.5% -10.4% 7.6%
(Demo&Vital) +Lab (Demo&Vital) +Lab+Med 3.2% 1.7% 1.7% 3.7% 0% -9.8%
(Demo&Vital) +Lab (Demo&Vital) +Lab+Med+Co-morbid 20.9% -1.7% 0% 1.8% -10.4% -8.1%
(Demo&Vital) +Lab+Med (Demo&Vital) +Lab+Med+Co-morbid 15.9% -3.3% -1.7% -1.7% -10.4% 1.8%
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Adding co-morbidities:
• decreased the AUC of other classifiers by 5.3% on average.
• increased the AUC of CPXC by 21.5% on average.
Performance changes when we add more variables
46. Ohio Center of Excellence in Knowledge-Enabled Computing
Application: Saturated Hydraulic
Conductivity
• Collaboration with University of Texas at Austin and USDA-ARS
• Problem definition:
1. Prediction of the soil water retention curve (SWRC)
2. Prediction of Saturated Hydraulic Conductivity (SHC)
3. Investigating the effect of sample dimensions on
prediction accuracy.
• Number of predictor variables: 6-13
• Number of response variables: 10
• 32 CPXR models are developed.
46
Behzad Ghanbarian, Vahid Taslimitehrani, Guozhu Dong, Yakov Pachepsky. Sample
dimensions effect on prediction of soil water retention curve and saturated hydraulic
conductivity. Journal of Hydrology. 528 (2015): 127-137.
48. Ohio Center of Excellence in Knowledge-Enabled Computing
Conclusion
• A new type of highly accurate and interpretable regression and classification
models, PXR/PXC are presented.
• New techniques to build PXR and PXC models are discussed.
• Each pair of pattern-model represents a diverse predictor-response interaction.
• PXR and PXC models are more accurate, interpretable and less overfitting than
other regression and classification algorithms.
• A new method adopted from CPXC presented to handle classifying imbalanced
datasets.
• Several applications of CPXR and CPXC are discussed.
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49. Ohio Center of Excellence in Knowledge-Enabled Computing
Related publications
• Guozhu Dong, Vahid Taslimitehrani, Pattern-Aided Regression Modeling and Prediction
Model Analysis. in IEEE Transactions on Knowledge and Data Engineering, vol.27, no.9,
pp.2452-2465, Sept. 1 2015.
• Vahid Taslimitehrani, Guozhu Dong. A New CPXR Based Logistic Regression Method
and Clinical Prognostic Modeling Results Using the Method on Traumatic Brain
Injury", IEEE International Conference on Bioinformatics and Bioengineering (BIBE),
2014, On page(s): 283 – 290 (Best Student Paper)
• Behzad Ghanbarian, Vahid Taslimitehrani, Guozhu Dong, Yakov Pachepsky. Sample
dimensions effect on prediction of soil water retention curve and saturated hydraulic
conductivity. Journal of Hydrology. 528 (2015): 127-137.
• Vahid Taslimitehrani, Guozhu Dong, Naveen Pereira, Maryam Panahiazar, Jyotishman
Pathak. Develolping HER-driven Heart Failure Models using CPXR(Log) with the
probabilistic loss function. Journal of Biomedical Informatics (2016).
• Guozhu Dong, Vahid Taslimitehrani, Pattern Aided Classification, SIAM Data Mining
Conference, 2016
49
50. Ohio Center of Excellence in Knowledge-Enabled Computing
Acknowledgement
50
Editor's Notes
Reference:
HF example, old and young patient
We propose a methodology that addresses those challenges.