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

Sarah Vluymans

ISBN 10: 3030046621 / ISBN 13: 9783030046620

Published by Springer International Publishing, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030046620

Bibliographic Details

• Title: Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
• Publisher: Springer International Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 3030046621
• ISBN 13: 9783030046620
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 21D millimeters 241 millimeters width by 160 millimeters height by 21 millimeters depth
• Weight: 571 grams

About this title

Synopsis

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.