Full Description
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.
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.
