How Fuzzy Concepts Contribute to Machine Learning: 416 - Rilegato

Eftekhari, Mahdi; Mehrpooya, Adel; Saberi-movahed, Farid; Torra, Vicen

Rilegato

ISBN 10: 3030940659 ISBN 13: 9783030940652

Casa editrice: Springer Nature, 2022

Riassunto

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

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Dalla quarta di copertina

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. 

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3030940659
• ISBN 13: 9783030940652
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 180
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania