Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods by Sarah VluymansEnglish | PDF,EPUB | 2019 | 263 Pages | ISBN : 3030046621 | 13.85 MB
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