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Studies in Computational Intelligence

Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods

Authors: Vluymans, Sarah

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  • Takes the research on ordered weighted average (OWA) fuzzy rough sets to the next level 
  • Provides clear guidelines on how to use them 
  • Expands the application to e.g. imbalanced, semi-supervised, multi-instance, and multi-label classification problems 
  • Each chapter is accompanied by a comprehensive experimental evaluation 
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eBook 117,69 €
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  • ISBN 978-3-030-04663-7
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Hardcover 145,59 €
price for Spain (gross)
  • ISBN 978-3-030-04662-0
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About this book

This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning.   The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.   

Table of contents (8 chapters)

Table of contents (8 chapters)

Buy this book

eBook 117,69 €
price for Spain (gross)
  • ISBN 978-3-030-04663-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 145,59 €
price for Spain (gross)
  • ISBN 978-3-030-04662-0
  • Free shipping for individuals worldwide. COVID-19 shipping restrictions apply.
  • Immediate ebook access, if available*, with your print order
  • Usually ready to be dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Authors
Series Title
Studies in Computational Intelligence
Series Volume
807
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-04663-7
DOI
10.1007/978-3-030-04663-7
Hardcover ISBN
978-3-030-04662-0
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XVIII, 249
Number of Illustrations
13 b/w illustrations, 10 illustrations in colour
Topics

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