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

  • Sarah Vluymans

Part of the Studies in Computational Intelligence book series (SCI, volume 807)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Sarah Vluymans
    Pages 1-16
  3. Sarah Vluymans
    Pages 17-35
  4. Sarah Vluymans
    Pages 37-80
  5. Sarah Vluymans
    Pages 81-110
  6. Sarah Vluymans
    Pages 131-187
  7. Sarah Vluymans
    Pages 189-218
  8. Sarah Vluymans
    Pages 219-226
  9. Back Matter
    Pages 227-249

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.


Computational Intelligence OWA Ordered Weighted Average Classification Multi-Instance Learning Multi-Label Learning

Authors and affiliations

  • Sarah Vluymans
    • 1
  1. 1.Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGentBelgium

Bibliographic information

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