Overview
- 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)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents(8 chapters)
About this book
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
Book Title: Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Authors: Sarah Vluymans
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-04663-7
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-04662-0Published: 05 December 2018
eBook ISBN: 978-3-030-04663-7Published: 23 November 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XVIII, 249
Number of Illustrations: 13 b/w illustrations, 10 illustrations in colour