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How fuzzy concepts contribute to machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	How fuzzy concepts contribute to machine learningby Mahdi Eftekhari ... [et al.].
other author:	Eftekhari, Mahdi.
Published:	Cham :Springer International Publishing :2022.
Description:	xii, 167 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-94066-9
ISBN:	9783030940669$q(electronic bk.)

How fuzzy concepts contribute to machine learning
How fuzzy concepts contribute to machine learning[electronic resource] /by Mahdi Eftekhari ... [et al.]. - Cham :Springer International Publishing :2022. - xii, 167 p. :ill., digital ;24 cm. - Studies in fuzziness and soft computing,v. 4161860-0808 ;. - Studies in fuzziness and soft computing ;v.273..

Chapter 1: Preliminaries -- Chapter 2: A Deﬁnition for Hesitant Fuzzy Partitions -- Chapter 3: Unsupervised Feature Selection Method. Chapter 4: Fuzzy Partitioning of Continuous Attributes -- Chapter 5: Comparing Different Stopping Criteria.

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.

ISBN: 9783030940669$q(electronic bk.)

Standard No.: 10.1007/978-3-030-94066-9doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31015113223

How fuzzy concepts contribute to machine learning
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505 0 $a Chapter 1: Preliminaries -- Chapter 2: A Deﬁnition for Hesitant Fuzzy Partitions -- Chapter 3: Unsupervised Feature Selection Method. Chapter 4: Fuzzy Partitioning of Continuous Attributes -- Chapter 5: Comparing Different Stopping Criteria.
520 $a 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.
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