How fuzzy concepts contribute to machine learning / Mahdi Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, Vicenç Torra.
Eftekhari, Mahdi, author.; Mehrpooya, Adel, author.; Saberi-Movahed, Farid, author.; Torra, Vicenç, author.
2022
Q325.5 .E48 2022
Available Online

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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
DOI https://doi.org/10.1007/978-3-030-94066-9
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.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Mehrpooya, Adel, author.
Saberi-Movahed, Farid, author.
Torra, Vicenç, author.
Series Studies in fuzziness and soft computing ; v. 416.
Available in Other Form How fuzzy concepts contribute to machine learning.
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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.

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