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: Hardback
Length: 167 pages
Publication date: 16 February 2022
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 data, enabling more accurate predictions and decision-making.

Multi-Criteria Decision Making: Explores how fuzzy and hesitant fuzzy sets can be integrated into multi-criteria decision-making frameworks to address complex problems with multiple objectives and criteria.

Applications in Machine Learning: Presents case studies and examples of how fuzzy and hesitant fuzzy sets have been applied in various machine learning tasks, such as image classification, spam detection, and medical diagnosis.

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 includes comprehensive explanations, examples, and exercises to help readers grasp the concepts and apply them in their own research and projects.

In conclusion, this book offers a valuable resource for anyone interested in exploring the application of fuzzy and hesitant fuzzy sets in machine learning. By leveraging these concepts, researchers and practitioners can gain a deeper understanding of algorithm behavior, improve the accuracy of predictions, and make more informed decisions in complex real-world situations.

Weight: 442g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030940652
Edition number: 1st ed. 2022