How Fuzzy Concepts Contribute to Machine Learning

Mahdi Eftekhari

ISBN 10: 3030940683 / ISBN 13: 9783030940683

Published by Springer International Publishing, 2023

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book introduces somecontemporaryapproacheson the application offuzzy and hesitant fuzzy sets in machine learning tasks such asclassification, clustering and dimensionreduction.Manysituationsarisein machine learning algorithmsinwhichapplying methods for uncertaintymodeling andmulti-criteriadecision making can lead toabetterunderstanding ofalgorithms behavior as well as achievinggood performances.Specifically,the present book is a collection of novel viewpointson howfuzzy andhesitant fuzzy conceptscan beappliedtodata uncertainty modeling aswell asbeing used to solvemulti-criteria decisionmaking challengesraised in machine learning problems. Using the multi-criteria decisionmaking framework, the book shows how different algorithms, rather thanhuman experts,areemployedto determine membership degrees. The book is expected to bring closerthe communities of pure mathematicians of fuzzysets and data scientists.

Seller Inventory # 9783030940683

Bibliographic Details

• Title: How Fuzzy Concepts Contribute to Machine Learning
• Publisher: Springer International Publishing
• 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.