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Sinopsis
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.
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Reseña del editor
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.
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Learning from Imbalanced Data Sets
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Learning from Imbalanced Data Sets
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a comprehensive review of imbalanced learning widely used worldwide in many real applications, such as fraud detection, disease diagnosis, etcProvides the user with the required background and software tools  needed to deal. Nº de ref. del artículo: 234946118
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Learning from Imbalanced Data Sets
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ISBN 10: 3319980734
ISBN 13: 9783319980737
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a general and comprehensibleoverview 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 considersthe different scenarios in Data Science for which the imbalanced classification cancreate a real challenge.This book stresses the gap with standard classification tasks by reviewing the casestudies and ad-hoc performance metrics that are applied in this area. It also covers thedifferent approaches that have been traditionally applied to address the binaryskewed class distribution. Specifically, it reviews cost-sensitive learning, data-levelpreprocessing methods and algorithm-level solutions, taking also into account thoseensemble-learning solutions that embed any of the former alternatives. Furthermore, itfocuses on the extension of the problem for multi-class problems, where the formerclassical methods are no longer to be applied in a straightforward way.This book also focuses on the data intrinsic characteristics that are the main causeswhich, added to the uneven class distribution, truly hinders the performance ofclassification algorithms in this scenario. Then, some notes on data reduction areprovided 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 attentionon the imbalanced data issue. Specifically, it considers the classification of data streams,non-classical classification problems, and the scalability related to Big Data. Examplesof software libraries and modules to address imbalanced classification are provided.This book is highly suitable for technical professionals, seniorundergraduate and graduatestudents in the areas of data science,computer science and engineering.It will also be useful for scientists and researchers to gain insight on the currentdevelopments in this area of study, as well as future research directions. 396 pp. Englisch. Nº de ref. del artículo: 9783319980737
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Learning from Imbalanced Data Sets
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Springer International Publishing, 2018
ISBN 10: 3319980734
ISBN 13: 9783319980737
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a general and comprehensibleoverview 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 considersthe different scenarios in Data Science for which the imbalanced classification cancreate a real challenge.This book stresses the gap with standard classification tasks by reviewing the casestudies and ad-hoc performance metrics that are applied in this area. It also covers thedifferent approaches that have been traditionally applied to address the binaryskewed class distribution. Specifically, it reviews cost-sensitive learning, data-levelpreprocessing methods and algorithm-level solutions, taking also into account thoseensemble-learning solutions that embed any of the former alternatives. Furthermore, itfocuses on the extension of the problem for multi-class problems, where the formerclassical methods are no longer to be applied in a straightforward way.This book also focuses on the data intrinsic characteristics that are the main causeswhich, added to the uneven class distribution, truly hinders the performance ofclassification algorithms in this scenario. Then, some notes on data reduction areprovided 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 attentionon the imbalanced data issue. Specifically, it considers the classification of data streams,non-classical classification problems, and the scalability related to Big Data. Examplesof software libraries and modules to address imbalanced classification are provided.This book is highly suitable for technical professionals, seniorundergraduate and graduatestudents in the areas of data science,computer science and engineering.It will also be useful for scientists and researchers to gain insight on the currentdevelopments in this area of study, as well as future research directions. Nº de ref. del artículo: 9783319980737
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