Learning From Imbalanced Data Sets

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Learning from Imbalanced Data Sets

Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera
Learning from Imbalanced Data Sets Book PDF

Author : Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera
Publisher : Springer
Page : 377 pages
File Size : 43,5 Mb
Release : 2018-10-22
Category : Computers
ISBN : 9783319980744

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Learning from Imbalanced Data Sets by Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera Pdf

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Imbalanced Learning

Haibo He,Yunqian Ma
Imbalanced Learning Book PDF

Author : Haibo He,Yunqian Ma
Publisher : John Wiley & Sons
Page : 222 pages
File Size : 40,5 Mb
Release : 2013-06-07
Category : Technology & Engineering
ISBN : 9781118646335

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Imbalanced Learning by Haibo He,Yunqian Ma Pdf

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Data Mining and Knowledge Discovery Handbook

Oded Maimon,Lior Rokach
Data Mining and Knowledge Discovery Handbook Book PDF

Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Page : 1378 pages
File Size : 47,6 Mb
Release : 2006-05-28
Category : Computers
ISBN : 9780387254654

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Data Mining and Knowledge Discovery Handbook by Oded Maimon,Lior Rokach Pdf

Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

Imbalanced Classification with Python

Jason Brownlee
Imbalanced Classification with Python Book PDF

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 463 pages
File Size : 47,8 Mb
Release : 2020-01-14
Category : Computers
ISBN : 8210379456XXX

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Imbalanced Classification with Python by Jason Brownlee Pdf

Imbalanced classification are those classification tasks where the distribution of examples across the classes is not equal. Cut through the equations, Greek letters, and confusion, and discover the specialized techniques data preparation techniques, learning algorithms, and performance metrics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently develop robust models for your own imbalanced classification projects.

Encyclopedia of Machine Learning

Claude Sammut,Geoffrey I. Webb
Encyclopedia of Machine Learning Book PDF

Author : Claude Sammut,Geoffrey I. Webb
Publisher : Springer Science & Business Media
Page : 1061 pages
File Size : 55,9 Mb
Release : 2011-03-28
Category : Computers
ISBN : 9780387307688

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Encyclopedia of Machine Learning by Claude Sammut,Geoffrey I. Webb Pdf

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics

Khosrow-Pour, D.B.A., Mehdi
Advanced Methodologies and Technologies in Network Architecture Mobile Computing and Data Analytics Book PDF

Author : Khosrow-Pour, D.B.A., Mehdi
Publisher : IGI Global
Page : 1857 pages
File Size : 54,8 Mb
Release : 2018-10-19
Category : Computers
ISBN : 9781522575993

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Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics by Khosrow-Pour, D.B.A., Mehdi Pdf

From cloud computing to data analytics, society stores vast supplies of information through wireless networks and mobile computing. As organizations are becoming increasingly more wireless, ensuring the security and seamless function of electronic gadgets while creating a strong network is imperative. Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics highlights the challenges associated with creating a strong network architecture in a perpetually online society. Readers will learn various methods in building a seamless mobile computing option and the most effective means of analyzing big data. This book is an important resource for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, and IT specialists seeking modern information on emerging methods in data mining, information technology, and wireless networks.

Machine Learning: ECML 2004

Jean-Francois Boulicaut,Floriana Esposito,Fosca Giannotti,Dino Pedreschi
Machine Learning ECML 2004 Book PDF

Author : Jean-Francois Boulicaut,Floriana Esposito,Fosca Giannotti,Dino Pedreschi
Publisher : Springer
Page : 582 pages
File Size : 45,5 Mb
Release : 2004-11-05
Category : Computers
ISBN : 9783540301158

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Machine Learning: ECML 2004 by Jean-Francois Boulicaut,Floriana Esposito,Fosca Giannotti,Dino Pedreschi Pdf

The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the Proceedings of the 8th European Conferences on Principles and Practice of Knowledge Discovery in Databases (LNAI 3202). The two conferences were co-located in Pisa, Tuscany, Italy during September 20–24, 2004. It was the fourth time in a row that ECML and PKDD were co-located. - ter the successful co-locations in Freiburg (2001), Helsinki (2002), and Cavtat- Dubrovnik (2003), it became clear that researchersstrongly supported the or- nization of a major scienti?c event about machine learning and data mining in Europe. We are happy to provide some statistics about the conferences. 581 di?erent papers were submitted to ECML/PKDD (about a 75% increase over 2003); 280 weresubmittedtoECML2004only,194weresubmittedtoPKDD2004only,and 107weresubmitted to both.Aroundhalfofthe authorsforsubmitted papersare from outside Europe, which is a clear indicator of the increasing attractiveness of ECML/PKDD. The Program Committee members were deeply involved in what turned out to be a highly competitive selection process. We assigned each paper to 3 - viewers, deciding on the appropriate PC for papers submitted to both ECML and PKDD. As a result, ECML PC members reviewed 312 papers and PKDD PC members reviewed 269 papers. We accepted for publication regular papers (45 for ECML 2004 and 39 for PKDD 2004) and short papers that were as- ciated with poster presentations (6 for ECML 2004 and 9 for PKDD 2004). The globalacceptance ratewas14.5%for regular papers(17% if we include the short papers).

Advances in Intelligent Data Analysis

Frank Hoffmann
Advances in Intelligent Data Analysis Book PDF

Author : Frank Hoffmann
Publisher : Springer Science & Business Media
Page : 395 pages
File Size : 50,8 Mb
Release : 2001-09-05
Category : Business & Economics
ISBN : 9783540425816

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Advances in Intelligent Data Analysis by Frank Hoffmann Pdf

Thismeantthat,ofthealmost150submissionswereceived,wewereableto selectonly23fororalpresentationand16forposterpresentation. Inaddition tothesecontributedpapers,therewasakeynoteaddressfromDarylPregibon, invitedpresentationsfromKatharinaMorik,RolfBackhofen,andSunilRao,and aspecial‘datachallenge’session,whereresearchersdescribedtheirattemptsto analyseachallengingdatasetprovidedbyPaulCohen. Thisacceptancerate enabledustoensureahighqualityconference,whilealsopermittingustop- videgoodcoverageofthevarioustopicssubsumedwithinthegeneralheading ofintelligentdataanalysis. Wewouldliketoexpressourthanksandappreciationtoeveryoneinvolved intheorganizationofthemeetingandtheselectionofthepapers. Itisthe behind-the-scenese?ortswhichensurethesmoothrunningandsuccessofany conference.

Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013)

Tutut Herawan,Mustafa Mat Deris,Jemal Abawajy
Proceedings of the First International Conference on Advanced Data and Information Engineering DaEng 2013 Book PDF

Author : Tutut Herawan,Mustafa Mat Deris,Jemal Abawajy
Publisher : Springer Science & Business Media
Page : 728 pages
File Size : 46,8 Mb
Release : 2013-12-14
Category : Technology & Engineering
ISBN : 9789814585187

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Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013) by Tutut Herawan,Mustafa Mat Deris,Jemal Abawajy Pdf

The proceeding is a collection of research papers presented at the International Conference on Data Engineering 2013 (DaEng-2013), a conference dedicated to address the challenges in the areas of database, information retrieval, data mining and knowledge management, thereby presenting a consolidated view to the interested researchers in the aforesaid fields. The goal of this conference was to bring together researchers and practitioners from academia and industry to focus on advanced on data engineering concepts and establishing new collaborations in these areas. The topics of interest are as follows but are not limited to: • Database theory • Data management • Data mining and warehousing • Data privacy & security • Information retrieval, integration and visualization • Information system • Knowledge discovery in databases • Mobile, grid and cloud computing • Knowledge-based • Knowledge management • Web data, services and intelligence

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

Rana, Dipti P.,Mehta, Rupa G.
Data Preprocessing Active Learning and Cost Perceptive Approaches for Resolving Data Imbalance Book PDF

Author : Rana, Dipti P.,Mehta, Rupa G.
Publisher : IGI Global
Page : 309 pages
File Size : 43,7 Mb
Release : 2021-06-04
Category : Computers
ISBN : 9781799873730

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Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance by Rana, Dipti P.,Mehta, Rupa G. Pdf

Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Data Democracy

Feras A. Batarseh,Ruixin Yang
Data Democracy Book PDF

Author : Feras A. Batarseh,Ruixin Yang
Publisher : Academic Press
Page : 266 pages
File Size : 41,6 Mb
Release : 2020-01-28
Category : Electronic
ISBN : 0128183667

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Data Democracy by Feras A. Batarseh,Ruixin Yang Pdf

This book provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world's best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy. - The future of the data republic, life within a data democracy, and our digital freedoms. - An in-depth analysis of open science, open data, open source software, and their future challenges. - A comprehensive review of data democracy's implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology. - The democratization of Artificial Intelligence (AI), and data issues such as: bias, imbalance, context, and knowledge extraction. - A systematic review of AI methods applied to software engineering problems.

Neural Information Processing

Bao-Liang Lu,Liqing Zhang,James Kwok
Neural Information Processing Book PDF

Author : Bao-Liang Lu,Liqing Zhang,James Kwok
Publisher : Springer Science & Business Media
Page : 799 pages
File Size : 52,9 Mb
Release : 2011-10-26
Category : Computers
ISBN : 9783642249570

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Neural Information Processing by Bao-Liang Lu,Liqing Zhang,James Kwok Pdf

The three volume set LNCS 7062, LNCS 7063, and LNCS 7064 constitutes the proceedings of the 18th International Conference on Neural Information Processing, ICONIP 2011, held in Shanghai, China, in November 2011. The 262 regular session papers presented were carefully reviewed and selected from numerous submissions. The papers of part I are organized in topical sections on perception, emotion and development, bioinformatics, biologically inspired vision and recognition, bio-medical data analysis, brain signal processing, brain-computer interfaces, brain-like systems, brain-realistic models for learning, memory and embodied cognition, Clifford algebraic neural networks, combining multiple learners, computational advances in bioinformatics, and computational-intelligent human computer interaction. The second volume is structured in topical sections on cybersecurity and data mining workshop, data mining and knowledge doscovery, evolutionary design and optimisation, graphical models, human-originated data analysis and implementation, information retrieval, integrating multiple nature-inspired approaches, Kernel methods and support vector machines, and learning and memory. The third volume contains all the contributions connected with multi-agent systems, natural language processing and intelligent Web information processing, neural encoding and decoding, neural network models, neuromorphic hardware and implementations, object recognition, visual perception modelling, and advances in computational intelligence methods based pattern recognition.

Machine Learning for Imbalanced Data

Kumar Abhishek,Dr. Mounir Abdelaziz
Machine Learning for Imbalanced Data Book PDF

Author : Kumar Abhishek,Dr. Mounir Abdelaziz
Publisher : Packt Publishing Ltd
Page : 344 pages
File Size : 44,9 Mb
Release : 2023-11-30
Category : Computers
ISBN : 9781801070881

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Machine Learning for Imbalanced Data by Kumar Abhishek,Dr. Mounir Abdelaziz Pdf

Take your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced data Key Features Understand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samples Learn cutting-edge deep learning techniques to overcome data imbalance Explore different methods for dealing with skewed data in ML and DL applications Purchase of the print or Kindle book includes a free eBook in the PDF format Book DescriptionAs machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance. Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods. As you progress, you’ll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that’ll demonstrate the practical implementation of each technique. By the end of this book, you’ll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.What you will learn Use imbalanced data in your machine learning models effectively Explore the metrics used when classes are imbalanced Understand how and when to apply various sampling methods such as over-sampling and under-sampling Apply data-based, algorithm-based, and hybrid approaches to deal with class imbalance Combine and choose from various options for data balancing while avoiding common pitfalls Understand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasets Who this book is for This book is for machine learning practitioners who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers/scientists, research scientists/engineers, and data scientists/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core machine learning concepts will help readers maximize the benefits and insights gained from this comprehensive resource.

Advanced Data Mining and Applications

Jie Tang,Irwin King,Ling Chen,Jianyong Wang
Advanced Data Mining and Applications Book PDF

Author : Jie Tang,Irwin King,Ling Chen,Jianyong Wang
Publisher : Springer Science & Business Media
Page : 437 pages
File Size : 41,7 Mb
Release : 2011-12-02
Category : Computers
ISBN : 9783642258527

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Advanced Data Mining and Applications by Jie Tang,Irwin King,Ling Chen,Jianyong Wang Pdf

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed proceedings of the 7th International Conference on Advanced Data Mining and Applications, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised full papers and 29 short papers presented together with 3 keynote speeches were carefully reviewed and selected from 191 submissions. The papers cover a wide range of topics presenting original research findings in data mining, spanning applications, algorithms, software and systems, and applied disciplines.

Machine Learning and Knowledge Discovery in Databases

Walter Daelemans,Bart Goethals
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Walter Daelemans,Bart Goethals
Publisher : Springer Science & Business Media
Page : 714 pages
File Size : 49,9 Mb
Release : 2008-09-04
Category : Computers
ISBN : 9783540874782

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Machine Learning and Knowledge Discovery in Databases by Walter Daelemans,Bart Goethals Pdf

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.