Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling: Theory and Applications

This book provides a comprehensive overview of fuzzy, intuitionistic fuzzy, and rough, fuzzy rough sets in data science, covering their theoretical background, applications, and techniques. It emphasizes the integration of machine learning methods to solve diverse problems, including pattern recognition, data mining, and bioinformatics. The book is designed for researchers, students, medical scientists, and professional engineers.

Format: Hardback
Length: 276 pages
Publication date: 26 March 2023
Publisher: Springer Verlag, Singapore

This comprehensive text offers a thorough exploration of the theoretical foundations and practical applications of fuzzy, intuitionistic fuzzy, and rough, fuzzy rough sets in the realm of data science. It serves as a valuable resource for scholars and practitioners seeking to leverage the power of these mathematical tools for data analysis and interpretation.

The book begins by providing a solid theoretical background, covering the fundamental concepts and principles of fuzzy, intuitionistic fuzzy, and rough sets. It delves into their theoretical foundations, properties, and applications, enabling readers to gain a deep understanding of these mathematical frameworks.

Next, the book showcases a wide range of individual, soft computing, optimization, and hybridization techniques of fuzzy and intuitionistic fuzzy sets with rough sets. It discusses various methods and algorithms for manipulating and analyzing fuzzy data, such as fuzzy clustering, fuzzy decision-making, and intuitionistic fuzzy optimization. These techniques find applications in diverse fields, including data handling, type-2 fuzzy systems, pattern recognition, data mining, and bioinformatics.

To address the challenges posed by real-world problems, the book emphasizes the effective implementation of machine learning techniques. It discusses various machine learning algorithms, such as neural networks, support vector machines, and random forests, and their applications in solving diverse problems in pattern recognition, data mining, and bioinformatics. These techniques enable the extraction of meaningful patterns and insights from complex data sets, facilitating decision-making processes and improving the accuracy of predictions.

Furthermore, the book highlights the theory and recent developments on uncertainty, fuzzy systems, feature extraction, text categorization, multiscale modeling, soft computing, machine learning, deep learning, SMOTE, data handling, decision-making, Diophantine fuzzy soft set, data envelopment analysis, centrally measures, social networks, Volterra–Fredholm integro-differential equation, Caputo fractional derivative, interval optimization, decision-making, classification problems. It provides a comprehensive overview of the latest research and advancements in these areas, enabling readers to stay updated with the latest developments in the field.

This book is primarily designed for researchers and students of data science, medical scientists, and professional engineers. It offers a comprehensive and up-to-date treatment of fuzzy, intuitionistic fuzzy, and rough, fuzzy rough sets, making it an essential resource for anyone interested in advancing their knowledge and expertise in this field.

With its clear and concise writing style, extensive illustrations, and practical examples, this book is accessible to a wide audience, regardless of their prior mathematical background. It serves as a valuable guide for those seeking to apply these mathematical tools to real-world problems and make informed decisions based on data-driven insights.

In conclusion, this book is a must-read for anyone interested in exploring the applications of fuzzy, intuitionistic fuzzy, and rough, fuzzy rough sets in data science. It provides a comprehensive and interdisciplinary approach to these mathematical tools, enabling readers to gain a deep understanding of their theoretical foundations, practical applications, and recent developments. By leveraging the power of these mathematical frameworks, researchers and practitioners can unlock the full potential of data analysis and make meaningful contributions to their respective fields.

Weight: 606g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811985652
Edition number: 1st ed. 2023