MOA is a framework for online learning from data streams. It is closely related to WEKA and includes tools for evaluation such as boosting, bagging, and Hoeffding Trees. MOA deals with evolving data streams and is easy to use and extend.
This document discusses the history and implementation of regression tree models. It begins by covering early tree models from the 1960s-1980s like CART and GUIDE. It then discusses more modern unified frameworks using modular packages in R like partykit and mob models. The document provides an example using a Bradley-Terry tree to model preferences from paired comparisons. It concludes by discussing potential extensions to deep learning methods.
Talk on the upcoming Mahout nearest neighbor framework focussing particularly on the k-means acceleration provided by the streaming k-means implementation.
BgN-Score & BsN-Score: Bagging & Boosting Based Ensemble Neural Networks Scoring Functions for Accurate Binding Affinity Prediction of Protein-Ligand Complexes
1) El documento discute los temas de ensambles, bagging, boosting y random forest. 2) Explica que los ensambles combinan múltiples modelos para mejorar el rendimiento y reducir el sesgo y la varianza. 3) Random forest aplica bagging a los árboles de decisión agregando aleatoriedad en la selección de variables para cada nodo con el fin de decorrelacionar los árboles.
This document discusses advanced tree-based machine learning methods including bagging, random forests, and boosting. Bagging involves resampling data and growing trees on each sample to average predictions and reduce variance compared to a single tree. Random forests build on bagging by randomly selecting features at each split. Boosting fits trees sequentially to emphasize training examples that previous trees misclassified to produce a stronger learner. These ensemble methods aggregate multiple tree models to improve over a single decision tree.
Machine Learning and Data Mining: 16 Classifiers EnsemblesPier Luca Lanzi
This document discusses ensemble machine learning methods. It introduces classifiers ensembles and describes three common ensemble methods: bagging, boosting, and random forests. For each method, it explains the basic idea, how the method works, advantages and disadvantages. Bagging constructs multiple classifiers from bootstrap samples of the training data and aggregates their predictions through voting. Boosting builds classifiers sequentially by focusing on misclassified examples. Random forests create decision trees with random subsets of features and samples. Ensembles can improve performance over single classifiers by reducing variance.
Decision Tree Ensembles - Bagging, Random Forest & Gradient Boosting MachinesDeepak George
This document provides a summary of Deepak George's background and experience in data science and machine learning. It includes information about his education such as degrees from College of Engineering Trivandrum and Indian Institute of Management Bangalore. It also lists his work experience at companies like Mu Sigma and Accenture Analytics. Additionally, it outlines some of Deepak's data science projects and accomplishments, as well as his contact information.
MOA is a framework for online learning from data streams. It is closely related to WEKA and includes tools for evaluation such as boosting, bagging, and Hoeffding Trees. MOA deals with evolving data streams and is easy to use and extend.
This document discusses the history and implementation of regression tree models. It begins by covering early tree models from the 1960s-1980s like CART and GUIDE. It then discusses more modern unified frameworks using modular packages in R like partykit and mob models. The document provides an example using a Bradley-Terry tree to model preferences from paired comparisons. It concludes by discussing potential extensions to deep learning methods.
Talk on the upcoming Mahout nearest neighbor framework focussing particularly on the k-means acceleration provided by the streaming k-means implementation.
BgN-Score & BsN-Score: Bagging & Boosting Based Ensemble Neural Networks Scoring Functions for Accurate Binding Affinity Prediction of Protein-Ligand Complexes
1) El documento discute los temas de ensambles, bagging, boosting y random forest. 2) Explica que los ensambles combinan múltiples modelos para mejorar el rendimiento y reducir el sesgo y la varianza. 3) Random forest aplica bagging a los árboles de decisión agregando aleatoriedad en la selección de variables para cada nodo con el fin de decorrelacionar los árboles.
This document discusses advanced tree-based machine learning methods including bagging, random forests, and boosting. Bagging involves resampling data and growing trees on each sample to average predictions and reduce variance compared to a single tree. Random forests build on bagging by randomly selecting features at each split. Boosting fits trees sequentially to emphasize training examples that previous trees misclassified to produce a stronger learner. These ensemble methods aggregate multiple tree models to improve over a single decision tree.
Machine Learning and Data Mining: 16 Classifiers EnsemblesPier Luca Lanzi
This document discusses ensemble machine learning methods. It introduces classifiers ensembles and describes three common ensemble methods: bagging, boosting, and random forests. For each method, it explains the basic idea, how the method works, advantages and disadvantages. Bagging constructs multiple classifiers from bootstrap samples of the training data and aggregates their predictions through voting. Boosting builds classifiers sequentially by focusing on misclassified examples. Random forests create decision trees with random subsets of features and samples. Ensembles can improve performance over single classifiers by reducing variance.
Decision Tree Ensembles - Bagging, Random Forest & Gradient Boosting MachinesDeepak George
This document provides a summary of Deepak George's background and experience in data science and machine learning. It includes information about his education such as degrees from College of Engineering Trivandrum and Indian Institute of Management Bangalore. It also lists his work experience at companies like Mu Sigma and Accenture Analytics. Additionally, it outlines some of Deepak's data science projects and accomplishments, as well as his contact information.
Slider: an Efficient Incremental Reasoner, by Jules Chevalieropencloudware
This document describes Slider, an efficient incremental reasoner for performing rule-based reasoning over semantic web data. Slider uses a parallel architecture with rule modules that can execute independently in parallel. It supports streaming data and handles duplicates more efficiently than previous approaches through vertical data partitioning and indexing. Experiments show Slider outperforms the baseline OWLIM reasoner, completing RDFS and OWL reasoning 71% and 106% faster on average respectively. Future work will focus on implementing additional rulesets and optimizing rule scheduling.
This document describes a machine learning framework for improving turbulence modelling in computational fluid dynamics simulations. The framework uses an unsupervised clustering algorithm to group flow field data into partitions, each representing a particular type of turbulence physics. Feature selection is applied to identify the most important variables for clustering. Models are then assigned to clusters using decision rules to enhance interpretability. The framework is tested on cases from an open turbulence dataset using k-means clustering, SHAP feature selection, and Skope-Rules induction. Visualizations of the clustering and feature importance are shown to validate the physics represented by each cluster.
1) OpenDA is a data assimilation toolbox that allows for both data assimilation and model calibration in a generic way.
2) It has an object oriented design that allows components like models and algorithms to be easily exchanged.
3) OpenDA supports parallel computing concepts and various ways of integrating models, including keeping models as "black boxes".
This document provides an introduction and overview of MATLAB (Matrix Laboratory). It outlines key topics that will be covered, including what MATLAB is, the MATLAB screen and workspace, variables and arrays, built-in math functions, flow control, and toolboxes. The document also lists some common commands and functions in MATLAB. It is intended to familiarize readers with the basics of MATLAB through examples and explanations of its main capabilities and features.
Fast Perceptron Decision Tree Learning from Evolving Data StreamsAlbert Bifet
The document proposes using perceptron learners at the leaves of Hoeffding decision trees to improve performance on data streams. It introduces a new evaluation metric called RAM-Hours that considers both time and memory usage. The authors empirically evaluate different classifier models, including Hoeffding trees with perceptron and naive Bayes learners at leaves, on several datasets. Results show that hybrid models like Hoeffding naive Bayes perceptron trees often provide the best balance of accuracy, time and memory usage.
Faceting Optimizations for Solr: Presented by Toke Eskildsen, State & Univers...Lucidworks
This document discusses optimizations for field faceting in Solr. It begins with an overview of faceting at web scale for the Danish Net Archive. Various techniques for optimizing faceting performance are then presented, including reusing counters, tracking updated counters, caching counters across shards, and using alternative counter structures like PackedInts and n-plane counters. The optimizations are shown to significantly speed up faceting for fields with high cardinality.
Large scale landuse classification of satellite imagerySuneel Marthi
This document summarizes a presentation on classifying land use from satellite imagery. It describes using a neural network to filter out cloudy images, segmenting images with a U-Net model to identify tulip fields, and implementing the workflow with Apache Beam for inference on new images. Examples are shown of detecting large and small tulip fields. Future work proposed includes classifying rock formations using infrared bands and measuring crop health.
ACAT 2019: A hybrid deep learning approach to vertexingHenry Schreiner
This document presents a hybrid deep learning approach for vertex finding in high-energy physics experiments. It uses a 1D convolutional neural network to analyze kernel density estimates of track information in order to identify primary vertex positions. The approach achieves primary vertex finding efficiencies of 88-94% with low false positive rates comparable to traditional algorithms. The authors demonstrate tuning of the efficiency-false positive rate tradeoff and discuss plans to improve performance by incorporating additional track information and iterative refinement.
Landuse Classification from Satellite Imagery using Deep LearningDataWorks Summit
With the abundance of remote sensing satellite imagery, the possibilities are endless as to the kind of insights that can be derived from them. One such use is to determine land use for agriculture and non-agricultural purposes.
In this talk, we’ll be looking at leveraging Sentinel-2 satellite imagery data along with OpenStreetMap labels to be able to classify land use as agricultural or non-agricultural.
Sentinel-2 data has a 10-meter resolution in RGB bands and is well-suited for land use classification. Using these two datasets, many different machine learning tasks can be performed like image segmentation into two classes (farm land and non-farm land) or more challenging task of identification of crop type being cultivated on fields.
For this talk, we’ll be looking at leveraging convolutional neural networks (CNNs) built with Apache MXNet to train deep learning models for land use classification. We’ll be covering the different deep learning architectures considered for this particular use case along with the appropriate metrics.
We’ll be leveraging streaming pipelines built on Apache Flink and Apache NiFi for model training and inference. Developers will come away with a better understanding of how to analyze satellite imagery and the different deep learning architectures along with their pros/cons when analyzing satellite imagery for land use. SUNEEL MARTHI and CHRIS OLIVIER, Software Development Engineer Amazon Web Services
An intro to explainable AI for polar climate scienceZachary Labe
26 March 2024…
GFDL Polar Climate Interest Group (Presentation): An intro to explainable AI for polar climate science, NOAA GFDL, Princeton, NJ.
References:
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002464
Subgraph Matching for Resource Allocation in the Federated Cloud EnvironmentAtakanAral
Federated clouds and cloud brokering allow migration
of virtual machines across clouds and even deployment
of cooperating VMs in different cloud data centers. In order
to fully benefit from these new opportunities, we propose a
heuristic that outputs a matching between virtual machine and
cloud data centers by taking resource capacities, VM topologies,
performance and resource costs into account. Results of
our initial evaluation using the CloudSim Framework indicate
that, proposed heuristic is promising for a better optimized
placement of networked VM groups onto the federated cloud
topology.
Modern machine learning methods that could be useful for particle physics.
Personal summary of the "Connecting the dots 2015" conference at Berkeley lab and ideas for what particle physics could try.
This document discusses approximate query processing using sampling to enable interactive queries over large datasets. It describes BlinkDB, a framework that creates and maintains samples from underlying data to return fast, approximate query answers with error bars. BlinkDB verifies the correctness of the error bars it returns by periodically replacing samples and using diagnostics to check the accuracy without running many queries. The document discusses challenges like selecting appropriate samples, estimating errors, and verifying results to balance speed, accuracy and correctness for interactive analysis of big data.
The document discusses the Virtual Atomic and Molecular Data Center (VAMDC) project, which aims to build an interoperable infrastructure for exchanging atomic and molecular data. VAMDC involves partners from multiple European countries and integrates several existing research databases. It intends to allow users to seamlessly search and retrieve data from over 20 atomic and molecular databases. VAMDC is developing standards like XSAMS, an XML schema, and technologies like the TAP protocol to enable interoperable data exchange between the distributed databases. The goal is to provide a virtual data warehouse to serve the needs of the atomic and molecular research community.
Talk given at Los Alamos National Labs in Fall 2015.
As research becomes more data-intensive and platforms become more heterogeneous, we need to shift focus from performance to productivity.
MATLAB is a high-level programming language and interactive environment for numerical computation, visualization, and programming. It is used widely in academia and industry in engineering and scientific fields. Some key points:
- MATLAB was developed by MathWorks and was originally created to provide easy access to matrix data and calculations.
- It includes a base program and various toolboxes for specialized tasks like fuzzy logic, neural networks, etc.
- MATLAB has a graphical user interface for interacting with data and visualizing results. It also allows programming in a matrix-based language.
- It is widely used for technical computing tasks like data analysis, modeling, simulation and algorithm development in many domains like engineering, science and education
The Society of Petroleum Engineers Distinguished Lecturer Program provides funding through member donations and industry support to bring expert lecturers to discuss emerging topics. This lecture discusses how big data analytics can help petroleum engineers and geoscientists reduce costs, improve productivity and efficiency by analyzing large datasets to find patterns and relationships. Case studies demonstrate applications in reservoir modeling, production optimization, and predictive maintenance.
Hivemall v0.5.0 was released on March 5, 2018 with new features including anomaly and change point detection algorithms, topic modeling capabilities, support for Spark 2.0/2.1/2.2, and a generic classifier/regressor. It also improved hyperparameter tuning, introduced feature hashing and binning, and added evaluation metrics and visualization tools. Future releases will focus on algorithms like XGBoost, LightGBM, and gradient boosting.
This document provides an outline and summaries for a two-day MATLAB workshop presented by Bhavesh Shah from 27-28 September 2012. The workshop will cover introductory topics such as what MATLAB is, the MATLAB screen interface, variables, arrays, matrices, built-in math functions, control structures, and toolboxes. It will also discuss more advanced topics like writing user-defined functions, neural networks, GUIs, and image processing. The goal is to introduce participants to the basics of using MATLAB for technical computing, modeling, simulation, and data analysis.
Artificial intelligence and data stream miningAlbert Bifet
Big Data and Artificial Intelligence have the potential to
fundamentally shift the way we interact with our surroundings. The
challenge of deriving insights from data streams has been recognized
as one of the most exciting and key opportunities for both academia
and industry. Advanced analysis of big data streams from sensors and
devices is bound to become a key area of artificial intelligence
research as the number of applications requiring such processing
increases. Dealing with the evolution over time of such data streams,
i.e., with concepts that drift or change completely, is one of the
core issues in stream mining. In this talk, I will present an overview
of data stream mining, industrial applications, open source tools, and
current challenges of data stream mining.
MOA is a framework for online machine learning from data streams. It includes algorithms for classification, regression, clustering and frequent pattern mining that can incorporate data and update models on the fly. MOA is closely related to WEKA and includes tools for evaluating streaming algorithms on data from sensors and IoT devices. It provides an environment for designing and running experiments on streaming machine learning algorithms at massive scales.
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This document describes Slider, an efficient incremental reasoner for performing rule-based reasoning over semantic web data. Slider uses a parallel architecture with rule modules that can execute independently in parallel. It supports streaming data and handles duplicates more efficiently than previous approaches through vertical data partitioning and indexing. Experiments show Slider outperforms the baseline OWLIM reasoner, completing RDFS and OWL reasoning 71% and 106% faster on average respectively. Future work will focus on implementing additional rulesets and optimizing rule scheduling.
This document describes a machine learning framework for improving turbulence modelling in computational fluid dynamics simulations. The framework uses an unsupervised clustering algorithm to group flow field data into partitions, each representing a particular type of turbulence physics. Feature selection is applied to identify the most important variables for clustering. Models are then assigned to clusters using decision rules to enhance interpretability. The framework is tested on cases from an open turbulence dataset using k-means clustering, SHAP feature selection, and Skope-Rules induction. Visualizations of the clustering and feature importance are shown to validate the physics represented by each cluster.
1) OpenDA is a data assimilation toolbox that allows for both data assimilation and model calibration in a generic way.
2) It has an object oriented design that allows components like models and algorithms to be easily exchanged.
3) OpenDA supports parallel computing concepts and various ways of integrating models, including keeping models as "black boxes".
This document provides an introduction and overview of MATLAB (Matrix Laboratory). It outlines key topics that will be covered, including what MATLAB is, the MATLAB screen and workspace, variables and arrays, built-in math functions, flow control, and toolboxes. The document also lists some common commands and functions in MATLAB. It is intended to familiarize readers with the basics of MATLAB through examples and explanations of its main capabilities and features.
Fast Perceptron Decision Tree Learning from Evolving Data StreamsAlbert Bifet
The document proposes using perceptron learners at the leaves of Hoeffding decision trees to improve performance on data streams. It introduces a new evaluation metric called RAM-Hours that considers both time and memory usage. The authors empirically evaluate different classifier models, including Hoeffding trees with perceptron and naive Bayes learners at leaves, on several datasets. Results show that hybrid models like Hoeffding naive Bayes perceptron trees often provide the best balance of accuracy, time and memory usage.
Faceting Optimizations for Solr: Presented by Toke Eskildsen, State & Univers...Lucidworks
This document discusses optimizations for field faceting in Solr. It begins with an overview of faceting at web scale for the Danish Net Archive. Various techniques for optimizing faceting performance are then presented, including reusing counters, tracking updated counters, caching counters across shards, and using alternative counter structures like PackedInts and n-plane counters. The optimizations are shown to significantly speed up faceting for fields with high cardinality.
Large scale landuse classification of satellite imagerySuneel Marthi
This document summarizes a presentation on classifying land use from satellite imagery. It describes using a neural network to filter out cloudy images, segmenting images with a U-Net model to identify tulip fields, and implementing the workflow with Apache Beam for inference on new images. Examples are shown of detecting large and small tulip fields. Future work proposed includes classifying rock formations using infrared bands and measuring crop health.
ACAT 2019: A hybrid deep learning approach to vertexingHenry Schreiner
This document presents a hybrid deep learning approach for vertex finding in high-energy physics experiments. It uses a 1D convolutional neural network to analyze kernel density estimates of track information in order to identify primary vertex positions. The approach achieves primary vertex finding efficiencies of 88-94% with low false positive rates comparable to traditional algorithms. The authors demonstrate tuning of the efficiency-false positive rate tradeoff and discuss plans to improve performance by incorporating additional track information and iterative refinement.
Landuse Classification from Satellite Imagery using Deep LearningDataWorks Summit
With the abundance of remote sensing satellite imagery, the possibilities are endless as to the kind of insights that can be derived from them. One such use is to determine land use for agriculture and non-agricultural purposes.
In this talk, we’ll be looking at leveraging Sentinel-2 satellite imagery data along with OpenStreetMap labels to be able to classify land use as agricultural or non-agricultural.
Sentinel-2 data has a 10-meter resolution in RGB bands and is well-suited for land use classification. Using these two datasets, many different machine learning tasks can be performed like image segmentation into two classes (farm land and non-farm land) or more challenging task of identification of crop type being cultivated on fields.
For this talk, we’ll be looking at leveraging convolutional neural networks (CNNs) built with Apache MXNet to train deep learning models for land use classification. We’ll be covering the different deep learning architectures considered for this particular use case along with the appropriate metrics.
We’ll be leveraging streaming pipelines built on Apache Flink and Apache NiFi for model training and inference. Developers will come away with a better understanding of how to analyze satellite imagery and the different deep learning architectures along with their pros/cons when analyzing satellite imagery for land use. SUNEEL MARTHI and CHRIS OLIVIER, Software Development Engineer Amazon Web Services
An intro to explainable AI for polar climate scienceZachary Labe
26 March 2024…
GFDL Polar Climate Interest Group (Presentation): An intro to explainable AI for polar climate science, NOAA GFDL, Princeton, NJ.
References:
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002464
Subgraph Matching for Resource Allocation in the Federated Cloud EnvironmentAtakanAral
Federated clouds and cloud brokering allow migration
of virtual machines across clouds and even deployment
of cooperating VMs in different cloud data centers. In order
to fully benefit from these new opportunities, we propose a
heuristic that outputs a matching between virtual machine and
cloud data centers by taking resource capacities, VM topologies,
performance and resource costs into account. Results of
our initial evaluation using the CloudSim Framework indicate
that, proposed heuristic is promising for a better optimized
placement of networked VM groups onto the federated cloud
topology.
Modern machine learning methods that could be useful for particle physics.
Personal summary of the "Connecting the dots 2015" conference at Berkeley lab and ideas for what particle physics could try.
This document discusses approximate query processing using sampling to enable interactive queries over large datasets. It describes BlinkDB, a framework that creates and maintains samples from underlying data to return fast, approximate query answers with error bars. BlinkDB verifies the correctness of the error bars it returns by periodically replacing samples and using diagnostics to check the accuracy without running many queries. The document discusses challenges like selecting appropriate samples, estimating errors, and verifying results to balance speed, accuracy and correctness for interactive analysis of big data.
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Talk given at Los Alamos National Labs in Fall 2015.
As research becomes more data-intensive and platforms become more heterogeneous, we need to shift focus from performance to productivity.
MATLAB is a high-level programming language and interactive environment for numerical computation, visualization, and programming. It is used widely in academia and industry in engineering and scientific fields. Some key points:
- MATLAB was developed by MathWorks and was originally created to provide easy access to matrix data and calculations.
- It includes a base program and various toolboxes for specialized tasks like fuzzy logic, neural networks, etc.
- MATLAB has a graphical user interface for interacting with data and visualizing results. It also allows programming in a matrix-based language.
- It is widely used for technical computing tasks like data analysis, modeling, simulation and algorithm development in many domains like engineering, science and education
The Society of Petroleum Engineers Distinguished Lecturer Program provides funding through member donations and industry support to bring expert lecturers to discuss emerging topics. This lecture discusses how big data analytics can help petroleum engineers and geoscientists reduce costs, improve productivity and efficiency by analyzing large datasets to find patterns and relationships. Case studies demonstrate applications in reservoir modeling, production optimization, and predictive maintenance.
Hivemall v0.5.0 was released on March 5, 2018 with new features including anomaly and change point detection algorithms, topic modeling capabilities, support for Spark 2.0/2.1/2.2, and a generic classifier/regressor. It also improved hyperparameter tuning, introduced feature hashing and binning, and added evaluation metrics and visualization tools. Future releases will focus on algorithms like XGBoost, LightGBM, and gradient boosting.
This document provides an outline and summaries for a two-day MATLAB workshop presented by Bhavesh Shah from 27-28 September 2012. The workshop will cover introductory topics such as what MATLAB is, the MATLAB screen interface, variables, arrays, matrices, built-in math functions, control structures, and toolboxes. It will also discuss more advanced topics like writing user-defined functions, neural networks, GUIs, and image processing. The goal is to introduce participants to the basics of using MATLAB for technical computing, modeling, simulation, and data analysis.
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Artificial intelligence and data stream miningAlbert Bifet
Big Data and Artificial Intelligence have the potential to
fundamentally shift the way we interact with our surroundings. The
challenge of deriving insights from data streams has been recognized
as one of the most exciting and key opportunities for both academia
and industry. Advanced analysis of big data streams from sensors and
devices is bound to become a key area of artificial intelligence
research as the number of applications requiring such processing
increases. Dealing with the evolution over time of such data streams,
i.e., with concepts that drift or change completely, is one of the
core issues in stream mining. In this talk, I will present an overview
of data stream mining, industrial applications, open source tools, and
current challenges of data stream mining.
MOA is a framework for online machine learning from data streams. It includes algorithms for classification, regression, clustering and frequent pattern mining that can incorporate data and update models on the fly. MOA is closely related to WEKA and includes tools for evaluating streaming algorithms on data from sensors and IoT devices. It provides an environment for designing and running experiments on streaming machine learning algorithms at massive scales.
Mining Big Data Streams with APACHE SAMOAAlbert Bifet
In this talk, we present Apache SAMOA, an open-source platform for
mining big data streams with Apache Flink, Storm and Samza. Real time analytics is
becoming the fastest and most efficient way to obtain useful knowledge
from what is happening now, allowing organizations to react quickly
when problems appear or to detect new trends helping to improve their
performance. Apache SAMOA includes algorithms for the most common
machine learning tasks such as classification and clustering. It
provides a pluggable architecture that allows it to run on Apache
Flink, but also with other several distributed stream processing
engines such as Storm and Samza.
Efficient Online Evaluation of Big Data Stream ClassifiersAlbert Bifet
The evaluation of classifiers in data streams is fundamental so that poorly-performing models can be identified, and either improved or replaced by better-performing models. This is an increasingly relevant and important task as stream data is generated from more sources, in real-time, in large quantities, and is now considered the largest source of big data. Both researchers and practitioners need to be able to effectively evaluate the performance of the methods they employ. However, there are major challenges for evaluation in a stream. Instances arriving in a data stream are usually time-dependent, and the underlying concept that they represent may evolve over time. Furthermore, the massive quantity of data also tends to exacerbate issues such as class imbalance. Current frameworks for evaluating streaming and online algorithms are able to give predictions in real-time, but as they use a prequential setting, they build only one model, and are thus not able to compute the statistical significance of results in real-time. In this paper we propose a new evaluation methodology for big data streams. This methodology addresses unbalanced data streams, data where change occurs on different time scales, and the question of how to split the data between training and testing, over multiple models.
Apache Samoa: Mining Big Data Streams with Apache FlinkAlbert Bifet
1) Apache SAMOA is a platform for mining big data streams in real-time that provides algorithms, libraries, and frameworks.
2) It allows researchers to develop and compare stream mining algorithms and practitioners to easily apply state-of-the-art algorithms to problems like sentiment analysis, spam detection, and recommendations.
3) A key challenge addressed by SAMOA is how to perform distributed stream mining on high-volume, high-velocity data streams at low latency using approaches like Apache Flink that can scale to handle large, fast data.
Data science involves extracting insights from large volumes of data. It is an interdisciplinary field that uses techniques from statistics, machine learning, and other domains. The document provides examples of classification algorithms like k-nearest neighbors, naive Bayes, and perceptrons that are commonly used in data science to build models for tasks like spam filtering or sentiment analysis. It also discusses clustering, frequent pattern mining, and other machine learning concepts.
This document provides an introduction to big data and MapReduce frameworks. It discusses:
- What big data is and examples of large datasets.
- An overview of MapReduce, including how it allows programmers to break problems into parallelizable map and reduce tasks.
- Details of how MapReduce frameworks like Apache Hadoop work, including distributed processing, fault tolerance, and the roles of mappers, reducers, and other components.
The document discusses data stream classification and algorithms for handling data streams. It begins with an introduction to data stream characteristics and challenges. It then discusses approximation algorithms for data streams, including maintaining statistics over sliding windows. Classification algorithms for data streams discussed include Naive Bayes classifiers, perceptrons, and Hoeffding trees, which are decision trees adapted for data streams using the Hoeffding bound inequality to determine the optimal split attribute.
The document discusses real-time big data management and Apache Flink. It provides an overview of Apache Flink, including its architecture, components, and APIs for batch and streaming data processing. It also provides examples of word count programs in Java, Scala, and Java 8 that demonstrate how to write Flink programs for batch and streaming data.
1. Real-time analytics of social networks can help companies detect new business opportunities by understanding customer needs and reactions in real-time.
2. MOA and SAMOA are frameworks for analyzing massive online and distributed data streams. MOA deals with evolving data streams using online learning algorithms. SAMOA provides a programming model for distributed, real-time machine learning on data streams.
3. Both tools allow companies to gain insights from social network and other real-time data to understand customers and react to opportunities.
Multi-label Classification with Meta-labelsAlbert Bifet
The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach suffers from class imbalance and a restricted hypothesis space that negatively affects its predictive performance, and can easily be outperformed by methods which learn labels together. A number of methods have grown around the label powerset approach, which models label combinations together as class values in a multi-class problem. We describe the label-powerset-based solutions under a general framework of \emph{meta-labels}. We provide theoretical justification for this framework which has been lacking, by viewing meta-labels as a hidden layer in an artificial neural network. We explain how meta-labels essentially allow a random projection into a space where non-linearities can easily be tackled with established linear learning algorithms. The proposed framework enables comparison and combination of related approaches to different multi-label problems. Indeed, we present a novel model in the framework and evaluate it empirically against several high-performing methods, with respect to predictive performance and scalability, on a number of datasets and evaluation metrics. Our deployment of an ensemble of meta-label classifiers obtains competitive accuracy for a fraction of the computation required by the current meta-label methods for multi-label classification.
Pitfalls in benchmarking data stream classification and how to avoid themAlbert Bifet
This document discusses pitfalls in benchmarking data stream classification and proposes ways to avoid them. It analyzes the electricity market dataset, a popular benchmark, and finds that it exhibits temporal dependence that favors classifiers that simply predict the previous value. It introduces new evaluation metrics like kappa plus that account for temporal dependence by comparing to a "no change" classifier. It also proposes a temporally aware classifier called SWT that incorporates previous labels into its predictions. Experiments on electricity and forest cover datasets show SWT and the new metrics better capture classifier performance on temporally dependent streaming data.
STRIP: stream learning of influence probabilities.Albert Bifet
This document presents a method called STRIP (Streaming Learning of Influence Probabilities) for learning influence probabilities between users in a social network from a streaming log of propagations. It describes three solutions: (1) storing the whole social graph in memory, (2) using min-wise independent hashing to estimate probabilities while using sublinear space, and (3) estimating probabilities only for the most active users to be more space efficient. Experimental results on a Twitter dataset showed these solutions provided good approximations while using reasonable memory and processing time.
Efficient Data Stream Classification via Probabilistic Adaptive WindowsAlbert Bifet
This document discusses efficient data stream classification using probabilistic adaptive windows. It introduces the concept of data streams which have potentially infinite sequences of high-speed data that must be processed in real-time with limited memory. It then describes the probabilistic approximate window (PAW) algorithm, which maintains a sample of data instances in logarithmic memory by giving greater weight to newer instances. The document evaluates several data stream classification methods on real and synthetic data streams and finds that k-nearest neighbors with PAW has higher accuracy and lower memory usage than other methods.
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
Streaming data analysis in real time is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. Evolving data streams are contributing to the growth of data created over the last few years. We are creating the same quantity of data every two days, as we created from the dawn of time up until 2003. Evolving data streams methods are becoming a low-cost, green methodology for real time online prediction and analysis. We discuss the current and future trends of mining evolving data streams, and the challenges that the field will have to overcome during the next years.
Mining Frequent Closed Graphs on Evolving Data StreamsAlbert Bifet
Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this talk we present a framework for studying graph pattern mining on time-varying streams and large datasets.
The document outlines a tutorial on handling concept drift in machine learning. It discusses the challenges of concept drift when applying supervised learning algorithms to streaming data where the underlying data distribution changes over time. The tutorial aims to provide an integrated view of adaptive learning methods and how they can handle concept drift. It covers topics such as the problem of concept drift, techniques for handling drift, evaluating adaptive learning approaches, and applications that experience concept drift.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
1. New ensemble methods for evolving data streams
A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavaldà
Laboratory for Relational Algorithmics, Complexity and Learning LARCA
UPC-Barcelona Tech, Catalonia
University of Waikato
Hamilton, New Zealand
Paris, 29 June 2009
15th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining 2009
2. New Ensemble Methods For Evolving Data Streams
Outline
a new experimental data stream framework for studying
concept drift
two new variants of Bagging:
ADWIN Bagging
Adaptive-Size Hoeffding Tree (ASHT) Bagging.
an evaluation study on synthetic and real-world datasets
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4. What is MOA?
{M}assive {O}nline {A}nalysis is a framework for online learning
from data streams.
It is closely related to WEKA
It includes a collection of offline and online as well as tools
for evaluation:
boosting and bagging
Hoeffding Trees
with and without Naïve Bayes classifiers at the leaves.
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5. WEKA
Waikato Environment for Knowledge Analysis
Collection of state-of-the-art machine learning algorithms
and data processing tools implemented in Java
Released under the GPL
Support for the whole process of experimental data mining
Preparation of input data
Statistical evaluation of learning schemes
Visualization of input data and the result of learning
Used for education, research and applications
Complements “Data Mining” by Witten & Frank
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7. MOA: the bird
The Moa (another native NZ bird) is not only flightless, like the
Weka, but also extinct.
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8. MOA: the bird
The Moa (another native NZ bird) is not only flightless, like the
Weka, but also extinct.
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9. MOA: the bird
The Moa (another native NZ bird) is not only flightless, like the
Weka, but also extinct.
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10. Data stream classification cycle
1 Process an example at a time,
and inspect it only once (at
most)
2 Use a limited amount of
memory
3 Work in a limited amount of
time
4 Be ready to predict at any
point
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11. Experimental setting
Evaluation procedures for Data
Streams
Holdout
Interleaved Test-Then-Train or
Prequential
Environments
Sensor Network: 100Kb
Handheld Computer: 32 Mb
Server: 400 Mb
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12. Experimental setting
Data Sources
Random Tree Generator
Random RBF Generator
LED Generator
Waveform Generator
Function Generator
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13. Experimental setting
Classifiers
Naive Bayes
Decision stumps
Hoeffding Tree
Hoeffding Option Tree
Bagging and Boosting
Prediction strategies
Majority class
Naive Bayes Leaves
Adaptive Hybrid
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14. Easy Design of a MOA classifier
void resetLearningImpl ()
void trainOnInstanceImpl (Instance inst)
double[] getVotesForInstance (Instance i)
void getModelDescription (StringBuilder
out, int indent)
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16. Extension to Evolving Data Streams
New Evolving Data Stream Extensions
New Stream Generators
New UNION of Streams
New Classifiers
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17. Extension to Evolving Data Streams
New Evolving Data Stream Generators
Random RBF with Drift Hyperplane
LED with Drift SEA Generator
Waveform with Drift STAGGER Generator
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18. Concept Drift Framework
f (t) f (t)
1
α
0.5
α
t
t0
W
Definition
Given two data streams a, b, we define c = a ⊕W b as the data
t0
stream built joining the two data streams a and b
Pr[c(t) = b(t)] = 1/(1 + e−4(t−t0 )/W ).
Pr[c(t) = a(t)] = 1 − Pr[c(t) = b(t)]
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19. Concept Drift Framework
f (t) f (t)
1
α
0.5
α
t
t0
W
Example
(((a ⊕W0 b) ⊕W1 c) ⊕W2 d) . . .
t0 t1 t2
(((SEA9 ⊕W SEA8 ) ⊕W0 SEA7 ) ⊕W0 SEA9.5 )
t0 2t 3t
CovPokElec = (CoverType ⊕5,000 Poker) ⊕5,000
581,012 1,000,000 ELEC2
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20. Extension to Evolving Data Streams
New Evolving Data Stream Classifiers
Adaptive Hoeffding Option Tree OCBoost
DDM Hoeffding Tree FLBoost
EDDM Hoeffding Tree
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22. Ensemble Methods
New ensemble methods:
Adaptive-Size Hoeffding Tree bagging:
each tree has a maximum size
after one node splits, it deletes some nodes to reduce its
size if the size of the tree is higher than the maximum value
ADWIN bagging:
When a change is detected, the worst classifier is removed
and a new classifier is added.
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23. Adaptive-Size Hoeffding Tree
T1 T2 T3 T4
Ensemble of trees of different size
smaller trees adapt more quickly to changes,
larger trees do better during periods with little change
diversity
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25. ADWIN Bagging
ADWIN
An adaptive sliding window whose size is recomputed online
according to the rate of change observed.
ADWIN has rigorous guarantees (theorems)
On ratio of false positives and negatives
On the relation of the size of the current window and
change rates
ADWIN Bagging
When a change is detected, the worst classifier is removed and
a new classifier is added.
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31. Summary
http://www.cs.waikato.ac.nz/∼abifet/MOA/
Conclusions
Extension of MOA to evolving data streams
MOA is easy to use and extend
New ensemble bagging methods:
Adaptive-Size Hoeffding Tree bagging
ADWIN bagging
Future Work
Extend MOA to more data mining and learning methods.
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