Eftekhari / Mehrpooya / Saberi-Movahed How Fuzzy Concepts Contribute to Machine Learning

Erscheinungsjahr 2022
ISBN: 978-3-030-94066-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 167 Seiten

Reihe: Studies in Fuzziness and Soft Computing

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. 

Zielgruppe

Research

Autoren/Hrsg.

Eftekhari, Mahdi

Mehrpooya, Adel

Saberi-Movahed, Farid

Torra, Vicenç

Inhaltsverzeichnis

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.