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Learning from Imbalanced Data Sets

Authors: Fernández Hilario, A., García, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F.

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  • Offers a comprehensive review of imbalanced learning widely used worldwide in many real applications, such as fraud detection, disease diagnosis, etc
  • Provides the user with the required background and software tools  needed to deal with Imbalance data
  • Presents the latest advances in the field of learning with imbalanced data, including Big Data applications and non-classical problems, such as semi-supervised learning, multilabel and multi instance learning, and ordinal classification and regression
  • Includes case studies
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eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-319-98074-4
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-319-98073-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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. 

Table of contents (14 chapters)

Table of contents (14 chapters)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-319-98074-4
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-319-98073-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Learning from Imbalanced Data Sets
Authors
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-319-98074-4
DOI
10.1007/978-3-319-98074-4
Hardcover ISBN
978-3-319-98073-7
Edition Number
1
Number of Pages
XVIII, 377
Number of Illustrations
21 b/w illustrations, 50 illustrations in colour
Topics