How Fuzzy Concepts Contribute to Machine Learning
Published on 15. February 2022
XII, 167 pages
978-3-030-94066-9 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XII, 167 p. 41 illus. in color.
File size
3,48 MB
ISBN-13
978-3-030-94066-9 (9783030940669)
DOI
10.1007/978-3-030-94066-9
Schweitzer Classification
Other editions
Additional editions
Mahdi Eftekhari | Adel Mehrpooya | Farid Saberi-Movahed
How Fuzzy Concepts Contribute to Machine Learning
Book
02/2022
Springer
€106.99
Shipment within 7-9 days
Content
Chapter 1: Preliminaries.- Chapter 2: A De?nition for Hesitant Fuzzy Partitions.- Chapter 3: Unsupervised Feature Selection Method. Chapter 4: Fuzzy Partitioning of Continuous Attributes.- Chapter 5: Comparing Different Stopping Criteria.
