How Fuzzy Concepts Contribute to Machine Learning

Mahdi Eftekhari

ISBN 10: 3030940683 / ISBN 13: 9783030940683

Published by Springer International Publishing, Springer International Publishing Feb 2023, 2023

About this Item

Neuware -This book introduces some contemporary approaches on the application of fuzzy and hesitant fuzzy sets in machine learning tasks such as classification, clustering and dimension reduction. Many situations arise in machine learning algorithms in which applying methods for uncertainty modeling and multi-criteria decision making can lead to a better understanding of algorithms behavior as well as achieving good performances. Specifically, the present book is a collection of novel viewpoints on how fuzzy and hesitant fuzzy concepts can be applied to data uncertainty modeling as well as being used to solve multi-criteria decision making challenges raised in machine learning problems. Using the multi-criteria decision making framework, the book shows how different algorithms, rather than human experts, are employed to determine membership degrees. The book is expected to bring closer the communities of pure mathematicians of fuzzy sets and data scientists.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch.

Seller Inventory # 9783030940683

Bibliographic Details

• Title: How Fuzzy Concepts Contribute to Machine Learning
• Publisher: Springer International Publishing, Springer International Publishing Feb 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 3030940683
• ISBN 13: 9783030940683
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This book introduces some contemporary approaches on the application of fuzzy and hesitant fuzzy sets in machine learning tasks such as classification, clustering and dimension reduction. Many situations arise in machine learning algorithms in which applying methods for uncertainty modeling and multi-criteria decision making can lead to a better understanding of algorithms behavior as well as achieving good performances. Specifically, the present book is a collection of novel viewpoints on how fuzzy and hesitant fuzzy concepts can be applied to data uncertainty modeling as well as being used to solve multi-criteria decision making challenges raised in machine learning problems. Using the multi-criteria decision making framework, the book shows how different algorithms, rather than human experts, are employed to determine membership degrees. The book is expected to bring closer the  communities of pure mathematicians of fuzzy sets and data scientists.