03748nam a22005535i 4500 978-3-031-66842-5 DE-He213 20251030113058.0 cr nn 008mamaa 240921s2024 sz | s |||| 0|eng d 9783031668425 978-3-031-66842-5 10.1007/978-3-031-66842-5 doi TA345-345.5 UN bicssc COM018000 bisacsh UN thema Machine Learning and Granular Computing: A Synergistic Design Environment [electronic resource] / edited by Witold Pedrycz, Shyi-Ming Chen. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. VIII, 352 p. 116 illus., 103 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Big Data, 2197-6511 ; 155 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. 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. Engineering Data processing. Computational intelligence. 64052 Machine learning. 55059 Data Engineering. 66059 Computational Intelligence. 64052 Machine Learning. 55059 Pedrycz, Witold. editor. (orcid)0000-0002-9335-9930 https://orcid.org/0000-0002-9335-9930 edt http://id.loc.gov/vocabulary/relators/edt 70097 Chen, Shyi-Ming. editor. edt http://id.loc.gov/vocabulary/relators/edt 84780 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031668418 Printed edition: 9783031668432 Printed edition: 9783031668449 Studies in Big Data, 2197-6511 ; 155 68724 https://doi.org/10.1007/978-3-031-66842-5 ZDB-2-INR ZDB-2-SXIT 63600 63600