Machine Learning and Granular Computing: A Synergistic Design Environment
This volume provides the reader with a comprehensive and up-to-date treatise positioned at the junction of the areas of Machine Learning (ML) and Granular Computing (GrC). ML offers a wealth of architectures and learning methods. Granular Computing addresses useful aspects of abstraction and knowledge representation that are of importance in the advanced design of ML architectures. In unison, ML and GrC support advances of the fundamental learning paradigm. As built upon synergy, this unified environment focuses on a spectrum of methodological and algorithmic issues, discusses implementations and elaborates on applications. The chapters bring forward recent developments showing ways of designing synergistic and coherently structured ML-GrC environment. The book will be of interest to a broad audience including researchers and practitioners active in the area of ML or GrC and interested in following its timely trends and new pursuits.

1145891196
Machine Learning and Granular Computing: A Synergistic Design Environment
This volume provides the reader with a comprehensive and up-to-date treatise positioned at the junction of the areas of Machine Learning (ML) and Granular Computing (GrC). ML offers a wealth of architectures and learning methods. Granular Computing addresses useful aspects of abstraction and knowledge representation that are of importance in the advanced design of ML architectures. In unison, ML and GrC support advances of the fundamental learning paradigm. As built upon synergy, this unified environment focuses on a spectrum of methodological and algorithmic issues, discusses implementations and elaborates on applications. The chapters bring forward recent developments showing ways of designing synergistic and coherently structured ML-GrC environment. The book will be of interest to a broad audience including researchers and practitioners active in the area of ML or GrC and interested in following its timely trends and new pursuits.

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Machine Learning and Granular Computing: A Synergistic Design Environment

Machine Learning and Granular Computing: A Synergistic Design Environment

Machine Learning and Granular Computing: A Synergistic Design Environment
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Machine Learning and Granular Computing: A Synergistic Design Environment

Machine Learning and Granular Computing: A Synergistic Design Environment

Overview

This volume provides the reader with a comprehensive and up-to-date treatise positioned at the junction of the areas of Machine Learning (ML) and Granular Computing (GrC). ML offers a wealth of architectures and learning methods. Granular Computing addresses useful aspects of abstraction and knowledge representation that are of importance in the advanced design of ML architectures. In unison, ML and GrC support advances of the fundamental learning paradigm. As built upon synergy, this unified environment focuses on a spectrum of methodological and algorithmic issues, discusses implementations and elaborates on applications. The chapters bring forward recent developments showing ways of designing synergistic and coherently structured ML-GrC environment. The book will be of interest to a broad audience including researchers and practitioners active in the area of ML or GrC and interested in following its timely trends and new pursuits.

Product Details

ISBN-13: 9783031668418
Publisher: Springer Nature Switzerland
Publication date: 10/23/2024
Series: Studies in Big Data , #155
Edition description: 2024
Pages: 352
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Shyi-Ming Chen is a Chair Professor in the Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan. He received the Ph.D. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in June 1991. He is an IEEE Fellow, an IET Fellow, an IFSA Fellow, an AAIA Fellow, an IETI Distinguished Fellow, and a Fellow of the Pakistan Academy of Engineering. He was the Dean of the College of Electrical Engineering and Computer Science, Jinwen University of Science and Technology, New Taipei City, Taiwan. He was the Vice President of the National Taichung University of Education, Taichung, Taiwan.

He was a Chair Professor in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He was the President of the Taiwanese Association for Artificial Intelligence (TAAI). He was the President of the Taiwanese Association for Consumer Electronics (TACE). He has published more than 600 papers in referred journals, conference proceedings and book chapters. His research interests include Fuzzy Systems, Intelligent Systems, Fuzzy Decision Making, Computational Intelligence, Knowledge-Based Systems, Machine Learning, Data Mining, Big Data Analysis, Genetic Algorithms, and Particle Swam Optimization Techniques. He is an Editor-in-Chief of Granular Computing, an Associate Editor of IEEE Transactions on Fuzzy Systems, an Associate Editor of IEEE Transactions on Cybernetics, an Associate Editor of IEEE Transactions on Artificial Intelligence, an Associate Editor of Knowledge-Based Systems, an Editorial Board Member of Information Fusion, an Associate Editor of Journal of Intelligent & Fuzzy Systems, an Associate Editor of International Journal on Artificial Intelligence Tools, an Associate Editor of International Journal of Pattern Recognition and Artificial Intelligence, an Associate Editor of International Journal of Fuzzy Systems, an Associate Editor of Journal of Information Science and Engineering, an Associate Editor of Fuzzy Optimization and Decision Making, an Associate Editor of Knowledge and Information Systems, an Editor of International Journal of Intelligent Systems, an Editor of Mathematical Problems in Engineering, and an Editor of Engineering applications of Artificial Intelligence.

Table of Contents

1. Explainability of Machine Learning Using Shapley Additive exPlanations (SHAP): CatBoost, XGBoost and LightGBM for Total Dissolved Gas Prediction.- 2. Explainable Deep Fuzzy Systems Applied to Sulfur Recovery Unit.- 3. Granular Fuzzy Model with High Order Singular Values Decomposition and Hesitation Fuzzy Granularity.- 4. Granular Trapezoidal Type-2 Shallow Fuzzy Neural Network.- 5. A Design of Multi-Granular Fuzzy Model with Hierarchical Tree Structure Using CFCM Clustering.- 6. Screening, Prediction and Remission of Depressive Disorder Using the Fuzzy Probability Function and Petri Net.

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