Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Published on 23. November 2018
XVIII, 249 pages
978-3-030-04663-7 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XVIII, 249 p. 23 illus., 10 illus. in color.
File size
4,75 MB
ISBN-13
978-3-030-04663-7 (9783030046637)
DOI
10.1007/978-3-030-04663-7
Schweitzer Classification
Other editions
Additional editions
Sarah Vluymans
Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Book
12/2018
Springer
€106.99
Shipment within 7-9 days
Content
Introduction.- Classi?cation.- Understanding OWA based fuzzy rough sets.- Fuzzy rough set based classi?cation of semi-supervised data.- Multi-instance learning.- Multi-label learning.- Conclusions and future work.- Bibliography.
