Skip to main content
SpringerLink
Log in

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

  • Book
  • © 2019

Overview

Authors:
  1. Sarah Vluymans

    1. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • 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

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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (8 chapters)

  1. Front Matter

    Pages i-xviii
    Download chapter PDF

  2. Introduction

    • Sarah Vluymans
    Pages 1-16

  3. Classification

    • Sarah Vluymans
    Pages 17-35

  4. Understanding OWA Based Fuzzy Rough Sets

    • Sarah Vluymans
    Pages 37-80

  5. Learning from Imbalanced Data

    • Sarah Vluymans
    Pages 81-110

  6. Fuzzy Rough Set Based Classification of Semi-supervised Data

    • Sarah Vluymans
    Pages 111-129

  7. Multi-instance Learning

    • Sarah Vluymans
    Pages 131-187

  8. Multi-label Learning

    • Sarah Vluymans
    Pages 189-218

  9. Conclusions and Future Work

    • Sarah Vluymans
    Pages 219-226

  10. Back Matter

    Pages 227-249
    Download chapter PDF
Back to top

Keywords

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.   


Authors and Affiliations

  • Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium

    Sarah Vluymans

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation