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.
Data Warehouses are structures with large amount of data collected from heterogeneous sources to be
used in a decision support system. Data Warehouses analysis identifies hidden patterns initially unexpected
which analysis requires great memory and computation cost. Data reduction methods were proposed to
make this analysis easier. In this paper, we present a hybrid approach based on Genetic Algorithms (GA)
as Evolutionary Algorithms and the Multiple Correspondence Analysis (MCA) as Analysis Factor Methods
to conduct this reduction. Our approach identifies reduced subset of dimensions p’ from the initial subset p
where p'<p where it is proposed to find the profile fact that is the closest to reference. Gas identify the
possible subsets and the Khi² formula of the ACM evaluates the quality of each subset. The study is based
on a distance measurement between the reference and n facts profile extracted from the warehouse.
Selecting the best stochastic systems for large scale engineering problemsIJECEIAES
Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Data Warehouses are structures with large amount of data collected from heterogeneous sources to be
used in a decision support system. Data Warehouses analysis identifies hidden patterns initially unexpected
which analysis requires great memory and computation cost. Data reduction methods were proposed to
make this analysis easier. In this paper, we present a hybrid approach based on Genetic Algorithms (GA)
as Evolutionary Algorithms and the Multiple Correspondence Analysis (MCA) as Analysis Factor Methods
to conduct this reduction. Our approach identifies reduced subset of dimensions p’ from the initial subset p
where p'<p where it is proposed to find the profile fact that is the closest to reference. Gas identify the
possible subsets and the Khi² formula of the ACM evaluates the quality of each subset. The study is based
on a distance measurement between the reference and n facts profile extracted from the warehouse.
Selecting the best stochastic systems for large scale engineering problemsIJECEIAES
Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
A pre conference workshop on Machine Learning was organized as a part of #doppa17, DevOps++ Global Summit 2017. The workshop was conducted by Dr. Vivek Vijay and Dr. Sandeep Yadav. All the copyrights are reserved with the author.
A smart environment is one that is able to identify people, interpret their actions, and react appropriately. Thus, one of the most important building blocks of smart environments is a person identification system. Face recognition devices are ideal for such systems, since they have recently become fast, cheap, unobtrusive, and, when combined with voice-recognition, are very robust against changes in the environment.
In this research, we broaden the advantages of nonnegative garrote as a feature selection method and empirically show it provides comparable results to panel models. We compare nonnegative garrote to other variable selection methods like ridge, lasso and adaptive lasso and analyze their performance on a dataset, which we have previously analyzed in another research. We conclude by showing that the results from nonnegative garrote are comparable to the robustness checks applied to the panel models to validate statistically significant variables. We conclude that nonnegative garrote is a robust variable selection method for panel orthonormal data as it accounts for the fixed and random effects, which are present in panel datasets.
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.
Bayesian Generalization Error and Real Log Canonical Threshold in Non-negativ...Naoki Hayashi
I have talked in the conference Algebraic Statistics 2020.
As a background of our research, I briefly explained singular learning theory which can be interpretable as an intersection between algebraic statistics and statistical learning theory.
The main part of this presentation is introducing our recent studies for parameter region restriction in singular learning theory. I showed the researches about the learning coefficient (real log canonical threshold) of NMF and LDA. NMF and LDA are typical models whose parameter regions are restricted.
Reduct generation for the incremental data using rough set theorycsandit
n today’s changing world huge amount of data is ge
nerated and transferred frequently.
Although the data is sometimes static but most comm
only it is dynamic and transactional. New
data that is being generated is getting constantly
added to the old/existing data. To discover the
knowledge from this incremental data, one approach
is to run the algorithm repeatedly for the
modified data sets which is time consuming. The pap
er proposes a dimension reduction
algorithm that can be applied in dynamic environmen
t for generation of reduced attribute set as
dynamic reduct.
The method analyzes the new dataset, when it become
s available, and modifies
the reduct accordingly to fit the entire dataset. T
he concepts of discernibility relation, attribute
dependency and attribute significance of Rough Set
Theory are integrated for the generation of
dynamic reduct set, which not only reduces the comp
lexity but also helps to achieve higher
accuracy of the decision system. The proposed metho
d has been applied on few benchmark
dataset collected from the UCI repository and a dyn
amic reduct is computed. Experimental
result shows the efficiency of the proposed method
Valencian Summer School 2015
Day 1
Lecture 3
Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
https://www.youtube.com/watch?v=b5qR4urr0vU&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=11
A mental representation or cognitive representation is a hypothetical internal cognitive symbol that represents external reality[1], or else a mental process that makes use of such a symbol: "a formal system for making explicit certain entities or types of information, together with a specification of how the system does this”[3]. To define the “Human Mental Representation”, four concepts have been described; Similarity, Analogy, Relationships at the Heart of Semantic Web. Similarity is defined as “learning information about one is generally true of the other”, and this becomes more and more true as the probability that the two causal/source variables is the same increases. The relationships used identifying similarities differs between experts and novices, with novices using surface features and experts using deeper structural relationships. Similarly, people relied on similarity mappings when the relational roles were more complex.
The purpose of categorization is twofold, to be able to infer the properties of the entity and to adapt the category itself. This description is essentially Piaget’s theory of development through assimilation and accommodation. Communication is similar to categorization, but rather than resolving for oneself, the issue is resolving new or developing shared concepts between people, which relates to many of the psycholinguistic conceptual grounding discussions (i.e., Herb Clark). Analogy is a special kind of similarity. Two situations are analogous if they share a common pattern of relationships among their constituent elements even though the elements themselves differ across the two situations. Typically, one analog, termed the source or base, is more familiar or better understood than the second analog, termed the target” (p. 117). Therefore, theoretical models of analogical inference need to focus on binding and mapping.
We explained the “Knowledge Representation”, and in the end, We provided the examples of “ Ontology and Knowledge Base” from Relationships at the Heart of Semantic Web p:15 [2].
References:
1- Chapters: 2, 3, 4, 6, Book: The Cambridge Handbook of Thinking and Reasoning (pp. 117-142). New York: Cambridge University Press. By By: Keith J. Holyoak and Robert G. Morrison
2- Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships, Book Title: Enhancing the Power of the Internet. By Amit Sheth, Ismailcem Budak Arpinar, Vipul Kashvap
3- Marr, David (2010). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. The MIT Press. ISBN 978-0262514620
https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
A pre conference workshop on Machine Learning was organized as a part of #doppa17, DevOps++ Global Summit 2017. The workshop was conducted by Dr. Vivek Vijay and Dr. Sandeep Yadav. All the copyrights are reserved with the author.
A smart environment is one that is able to identify people, interpret their actions, and react appropriately. Thus, one of the most important building blocks of smart environments is a person identification system. Face recognition devices are ideal for such systems, since they have recently become fast, cheap, unobtrusive, and, when combined with voice-recognition, are very robust against changes in the environment.
In this research, we broaden the advantages of nonnegative garrote as a feature selection method and empirically show it provides comparable results to panel models. We compare nonnegative garrote to other variable selection methods like ridge, lasso and adaptive lasso and analyze their performance on a dataset, which we have previously analyzed in another research. We conclude by showing that the results from nonnegative garrote are comparable to the robustness checks applied to the panel models to validate statistically significant variables. We conclude that nonnegative garrote is a robust variable selection method for panel orthonormal data as it accounts for the fixed and random effects, which are present in panel datasets.
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.
Bayesian Generalization Error and Real Log Canonical Threshold in Non-negativ...Naoki Hayashi
I have talked in the conference Algebraic Statistics 2020.
As a background of our research, I briefly explained singular learning theory which can be interpretable as an intersection between algebraic statistics and statistical learning theory.
The main part of this presentation is introducing our recent studies for parameter region restriction in singular learning theory. I showed the researches about the learning coefficient (real log canonical threshold) of NMF and LDA. NMF and LDA are typical models whose parameter regions are restricted.
Reduct generation for the incremental data using rough set theorycsandit
n today’s changing world huge amount of data is ge
nerated and transferred frequently.
Although the data is sometimes static but most comm
only it is dynamic and transactional. New
data that is being generated is getting constantly
added to the old/existing data. To discover the
knowledge from this incremental data, one approach
is to run the algorithm repeatedly for the
modified data sets which is time consuming. The pap
er proposes a dimension reduction
algorithm that can be applied in dynamic environmen
t for generation of reduced attribute set as
dynamic reduct.
The method analyzes the new dataset, when it become
s available, and modifies
the reduct accordingly to fit the entire dataset. T
he concepts of discernibility relation, attribute
dependency and attribute significance of Rough Set
Theory are integrated for the generation of
dynamic reduct set, which not only reduces the comp
lexity but also helps to achieve higher
accuracy of the decision system. The proposed metho
d has been applied on few benchmark
dataset collected from the UCI repository and a dyn
amic reduct is computed. Experimental
result shows the efficiency of the proposed method
Valencian Summer School 2015
Day 1
Lecture 3
Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
https://www.youtube.com/watch?v=b5qR4urr0vU&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=11
A mental representation or cognitive representation is a hypothetical internal cognitive symbol that represents external reality[1], or else a mental process that makes use of such a symbol: "a formal system for making explicit certain entities or types of information, together with a specification of how the system does this”[3]. To define the “Human Mental Representation”, four concepts have been described; Similarity, Analogy, Relationships at the Heart of Semantic Web. Similarity is defined as “learning information about one is generally true of the other”, and this becomes more and more true as the probability that the two causal/source variables is the same increases. The relationships used identifying similarities differs between experts and novices, with novices using surface features and experts using deeper structural relationships. Similarly, people relied on similarity mappings when the relational roles were more complex.
The purpose of categorization is twofold, to be able to infer the properties of the entity and to adapt the category itself. This description is essentially Piaget’s theory of development through assimilation and accommodation. Communication is similar to categorization, but rather than resolving for oneself, the issue is resolving new or developing shared concepts between people, which relates to many of the psycholinguistic conceptual grounding discussions (i.e., Herb Clark). Analogy is a special kind of similarity. Two situations are analogous if they share a common pattern of relationships among their constituent elements even though the elements themselves differ across the two situations. Typically, one analog, termed the source or base, is more familiar or better understood than the second analog, termed the target” (p. 117). Therefore, theoretical models of analogical inference need to focus on binding and mapping.
We explained the “Knowledge Representation”, and in the end, We provided the examples of “ Ontology and Knowledge Base” from Relationships at the Heart of Semantic Web p:15 [2].
References:
1- Chapters: 2, 3, 4, 6, Book: The Cambridge Handbook of Thinking and Reasoning (pp. 117-142). New York: Cambridge University Press. By By: Keith J. Holyoak and Robert G. Morrison
2- Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships, Book Title: Enhancing the Power of the Internet. By Amit Sheth, Ismailcem Budak Arpinar, Vipul Kashvap
3- Marr, David (2010). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. The MIT Press. ISBN 978-0262514620
https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
In this chapter the author want to find out, how can an average human being can become an Expert in a specific field, and He highlights the common traits of the experts such as:
expert see the world differently, which the non-experts can’t see
An Expert in a specific field has a superior memory for the details of that field
Most importantly experts overcomes the brain’s most famous constraint “7”
Author says that in the field of remembering an average human can hold upto 7 plus or minus 2 digits in his brain at time. This the capacity of our short term memories by which we are limited but an expert is not limited by this constraint. When an expert look at a number, he does not see just the number rather he sees a memory or an image from the past such as Birth date or any memory which is related to the number. In the chapter author explains this difference between an average human and expert more clearly with examples such as chicken sexers and Swat officers
Fuzzy modeling is a powerful approach found by Zadeh for the modeling of complex and uncertain systems [2]. Fuzzy logic has a distinctive advantage where the precise definition of a control process is unachievable. Fuzzy models have the ability to establish a relationship between input and output variables by employing predefined rules. The technique provides simple solutions which are based on natural language statements. Fuzzy logic takes the inputs and outputs in the form of fuzzy sets where each set contains elements that have varying degrees of membership. A fuzzy set then enables transforming real numbers to the membership degrees changing from 0 to 1. Fuzzy rules relate input variables to output variables. These rules represent the expert knowledge in the system. Indeed, the intuition behind fuzzy logic is, it works with perception-based data instead of measurement-based which are crisp and numeric. Hence, it tries to capture how human use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Perceptions in this manner are inherently imprecise when compared to crisp values, for example, a human might express his intuition about the weather as being not very hot while a sensor would read the heat in degrees and give us a crisp value. Therefore, perceptions are very subjective and reflect the partiality of human concepts.
In 2001, Prof. Zadeh proposed his computational theory of perceptions (CTP) where the objects of computations are words and propositions drawn from natural language rather than crisp numeric values. The idea of the theory came due to the unavailability of a methodology for reasoning and computing with perceptions rather than measurements. Hence, the CPT was the ground for allowing a computer to make subjective judgments which often refered as perceptual computing.
E.H. Mamdani, Application of fuzzy algorithms for control of simple dynamic plant, in: Proceedings of the Institution of Electrical Engineers, IET, 1974, pp. 1585-1588.
Zadeh, Lotfi A. "Fuzzy sets." Information and control 8, no. 3 (1965): 338-353.
Zadeh, Lotfi A. "A new direction in AI: Toward a computational theory of perceptions." AI magazine 22, no. 1 (2001): 73.
https://www.youtube.com/watch?v=wbXEXGT3I9I&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=8
Link of video:
https://www.youtube.com/watch?v=wbXEXGT3I9I
This is a review of the keynote presented by Eric Horvitz, Managing Director, Microsoft, Redmond.
This keynote was presented at Computing Community Consortium in Washington DC on June-07-2016.
Eric has discussed about 3 things in his keynote: Healthcare, Agriculture and Transport.
Mainly he has focussed on Health care.
The goal of AI
Broad Spectrum of Opportunities for AI
Healthcare
Sciences
Transportation
Agriculture
Sustainability
Education
Governance
Criminal justice
Privacy & security
Emergency management
A work conducted in John Hopkins University
References:
http://research.microsoft.com/en-us/um/people/horvitz/AI_supporting_people_and_society_Eric_Horvitz.pdf
https://www.youtube.com/watch?v=rek3jjbYRLo
https://en.wikipedia.org/wiki/Artificial_intelligence
https://en.wikipedia.org/wiki/AI_winter
http://research.microsoft.com/en-us/um/people/horvitz/
Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.
Link to the paper - http://knoesis.org/?q=node/2754
https://www.youtube.com/watch?v=fmZDRL9P-v4&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=9
To make an autonomous vehicle more cognitive, it needs the implementation of advanced cognition theories and AI theories. In this work, we firstly make a brief overview of current advanced theories of cognition in Psychology and Computer Science. Then we mainly analyze and compare the architectures of the autonomous vehicles winning DARPA Challenges. The layout of sensors and the design of software system are critical to the winning autonomous vehicles. By comparing different autonomous vehicles, we find some common points shared among them and more differences due to the various sensors layouts and the difference among cognition architectures, which could give some valuable directions to the researchers in both computer science and cognition fields. Then we will link decision-making to intelligence decision-making and its algorithm using example.
The is an active field of research in understanding the cognitive approach in dreaming. Analysing these dreams will give us the intuition on how the brains works and helps us in improving the technology. Calvin hall (1909-1985) developed the first scientific theories of dream interpretation based on quantitative analysis in 1953. According to him, the images in the dreams are the concrete embodiment of dreamer’s thought, these images give visual expression to that which is invisible, namely, conceptions. These conceptions can be about ourselves, others, environment, penalties and conflicts. Calvin hall says that there is a continuity between a person's’ wakefulness and their dream experience. He believed that during dreams we express creativity, similar to what we would do when expressing ourselves through metaphors in poetry.
With this as the basis, there are several theories which try to explain the concept of dreaming and its effect. Once such theory is by Dr. Robert Stickgold who is an Associate Professor of Psychiatry at Harvard Medical School and Beth Israel Deaconess Medical Centre. According to him, brain will extract from experiences to gist of what happened and kind of forget all those details that weren’t that important. It takes large amount of experiences and take them all together and figure out what rules and explain how our world work. When we are sleeping your brain is pulling everything together and seeing how it fits together and how to summarize it. Dreams also act as predictors that the brain is going to do what it needs to do to really figure out the problem. One can gain these insights when we don’t even know where to find just by sleeping.
One takeaway Dr. Sheth pointed out was the importance of abstraction in pulling the parts of the dreams together. For a example given blood, medicine and nurse as difference pieces, one would imagine a hospital and another would assume a location of a blast. This gives the notion of personalization and/or contextualization during the process of abstraction.
Understanding of Electronic Medical Records(EMRs) plays a crucial role in improving healthcare outcomes. However, the unstructured nature of EMRs poses several technical challenges for structured information extraction from clinical notes leading to automatic analysis. Natural Language Processing(NLP) techniques developed to process EMRs are effective for variety of tasks, they often fail to preserve the semantics of original information expressed in EMRs, particularly in complex scenarios. This paper illustrates the complexity of the problems involved and deals with conflicts created due to the shortcomings of NLP techniques and demonstrates where domain specific knowledge bases can come to rescue in resolving conflicts that can significantly improve the semantic annotation and structured information extraction. We discuss various insights gained from our study on real world dataset.
Gang affiliates have joined the masses who use social media to share thoughts and actions. Perhaps paradoxically, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation gang related crimes and activities. This talk discusses our efforts in analyzing street gangs on twitter, with an emphasis on discovering their profiles. Our approach, which uses deep learning to embed signals in tweet language, images, shared YouTube links, and emoji use into a vector space for machine learning classifiers, recovers gang member profiles with promising accuracy and a low false positive rate.
With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is to extract and normalize entity mentions. State-of-the-art approaches have focused on the recognition of entities that are explicitly mentioned in a sentence. However, clinical documents often contain phrases that indicate the entities but do not contain their names. We term those implicit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to create entity models, projects sentences to the entity models and identifies implicit entity mentions by evaluating semantic similarity between sentences and entity models. The evaluation with 857 sentences selected for 8 different entities shows that our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping it to improve over the baselines, and achieving F1 scores of .81 and .73 for different classes of implicit mentions. Our gold standard annotations are made available to encourage further research in the area of IER.
https://www.youtube.com/watch?v=uBijGs1NJCE&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=13
Semantic and AI research communities have a strong body of work focuses on extracting facts from the web automatically and represent them in a graph based representation. NELL and Knowledge Vault are two prominent knowledge graphs of that kind. However, due to the inherent noise of the web the resulting knowledge also contain noisy data. With the huge volume of the facts extracted from the web, it is impractical to use traditional reasoning approaches to capture the inconsistencies in these knowledge graphs. This work addresses this issue by using semantics in the form of schema knowledge together with statistics in the form of confidence value of facts derived from information extraction techniques. They use probabilistic soft logic which is a recently introduced statistical learning approach which allows to assign weights to the logical statement and their dependencies. The weighted soft logic rules are represented in a probabilistic graphical model with their dependencies to identify the different interpretations of a KG and pick the most consistent KG.
References
Pujara, Jay, et al. "Using Semantics and Statistics to Turn Data into Knowledge." AI Magazine 36.1 (2015): 65-74.
Pujara, Jay, et al. "Knowledge graph identification." International Semantic Web Conference. Springer Berlin Heidelberg, 2013.
Lise Getoor “Combining Statistics and Semantics to Turn Data into Knowledge” ESWC Keynote 2015
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members’ social media posts.
Natural language text can have explicit and implicit constructs. In this presentation we discuss how to link the entities mentioned in an implicit manner in tweets.
Big Data Challenges and Trust Management: A Personal Perspective
A tutorial presented by Dr. Krishnaprasad Thirunarayan at the International Conference on Collaboration Technologies and Systems 2016 (CTS 2016)
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.
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/
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.
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.
It is the concept of Data mining and knowledge discovery in Databases(KDD)..
BIODATA:
I am sameer kumar das working as an asst.professor in CSE at GATE,Odisha and i contd.PhD in Engineering.Thanks
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".
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• 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?
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Vahid Taslimitehrani PhD Dissertation Defense: 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).
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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
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
…
…
…
…
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
…
…
…
…
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
40
• 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
44
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%
45
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.
48
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.