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

• Main Author: Vluymans, Sarah (Author, http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Studies in Computational Intelligence, 807
• Subjects: Computational intelligence.; Artificial intelligence.; Computational Intelligence.; Artificial Intelligence.

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...

Table of Contents:

• Introduction
• Classification
• Understanding OWA based fuzzy rough sets
• Fuzzy rough set based classification of semi-supervised data
• Multi-instance learning
• Multi-label learning
• Conclusions and future work
• Bibliography.