How Fuzzy Concepts Contribute to Machine Learning

This book discusses modern techniques for using fuzzy and hesitant fuzzy sets in machine learning, such as classification, clustering, and dimension reduction. It shows how these concepts can be applied to data uncertainty modeling and multi-criteria decision-making, and how different algorithms can be used to determine membership degrees. The book aims to bring together the communities of fuzzy set theory and data science.

Format: Paperback / softback
Length: 167 pages
Publication date: 17 February 2023
Publisher: Springer Nature Switzerland AG

This book delves into cutting-edge approaches to leveraging fuzzy and hesitant fuzzy sets in machine learning tasks, including classification, clustering, and dimension reduction. In numerous scenarios within machine learning algorithms, the application of methods for uncertainty modeling and multi-criteria decision-making can enhance our understanding of algorithm behavior and ultimately result in improved performance. Specifically, this book presents a compilation of novel viewpoints on how fuzzy and hesitant fuzzy concepts can be applied to data uncertainty modeling, as well as utilized to tackle multi-criteria decision-making challenges encountered in machine learning problems.

The book adopts a multi-criteria decision-making framework to demonstrate how different algorithms, rather than human experts, are employed to determine membership degrees. By doing so, it aims to bridge the gap between the communities of pure mathematicians specializing in fuzzy sets and data scientists working with real-world data.

The book covers a wide range of topics, including:

Introduction to Fuzzy and Hesitant Fuzzy Sets: Provides a comprehensive overview of these mathematical concepts and their applications in machine learning.

Uncertainty Modeling: Discusses how fuzzy and hesitant fuzzy sets can be used to represent and handle uncertain information in data.

Multi-Criteria Decision Making: Presents various algorithms and techniques for solving multi-criteria decision-making problems, such as decision trees, linear programming, and optimization.

Case Studies: Includes real-world case studies and applications to demonstrate the practical effectiveness of fuzzy and hesitant fuzzy sets in various machine learning tasks.

The book is written in a clear and accessible style, making it suitable for both researchers and practitioners in the field of machine learning. It provides valuable insights and practical guidance for those interested in exploring the potential of fuzzy and hesitant fuzzy sets in their research and applications.

Weight: 285g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030940683
Edition number: 1st ed. 2022