How fuzzy concepts contribute to machine learning / Mahdi Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, Vicenç Torra.
2022
Q325.5 .E48 2022
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Title
How fuzzy concepts contribute to machine learning / Mahdi Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, Vicenç Torra.
Author
Eftekhari, Mahdi, author.
ISBN
9783030940669 (electronic bk.)
3030940667 (electronic bk.)
9783030940652
3030940659
3030940667 (electronic bk.)
9783030940652
3030940659
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : color illustrations.
Item Number
10.1007/978-3-030-94066-9 doi
Call Number
Q325.5 .E48 2022
Dewey Decimal Classification
006.3/1015113223
Summary
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. .
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Series
Studies in fuzziness and soft computing ; v. 416.
Available in Other Form
How fuzzy concepts contribute to machine learning.
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Table of Contents
Chapter 1: Preliminaries
Chapter 2: A Denition for Hesitant Fuzzy Partitions
Chapter 3: Unsupervised Feature Selection Method. Chapter 4: Fuzzy Partitioning of Continuous Attributes
Chapter 5: Comparing Different Stopping Criteria.
Chapter 2: A Denition for Hesitant Fuzzy Partitions
Chapter 3: Unsupervised Feature Selection Method. Chapter 4: Fuzzy Partitioning of Continuous Attributes
Chapter 5: Comparing Different Stopping Criteria.