03507nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172072001500188072002300203072001400226245014600240250001800386264006900404300006800473336002600541337002600567338003600593347002400629490004300653505060700696520095201303650003402255650003902289650002902328650002902357650003902386650002902425700015202454700008502606710003402691773002602725776003602751776003602787776003602823830005002859856004602909912001402955912001502969999001702984978-3-031-66842-5DE-He21320251030113058.0cr nn 008mamaa240921s2024    sz |    s    |||| 0|eng d  a97830316684259978-3-031-66842-57 a10.1007/978-3-031-66842-52doi 4aTA345-345.5 7aUN2bicssc 7aCOM0180002bisacsh 7aUN2thema10aMachine Learning and Granular Computing: A Synergistic Design Environmenth[electronic resource] /cedited by Witold Pedrycz, Shyi-Ming Chen.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aVIII, 352 p. 116 illus., 103 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aStudies in Big Data,x2197-6511 ;v1550 a1. 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.  aThis 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. 0aEngineeringxData processing. 0aComputational intelligence.964052 0aMachine learning.95505914aData Engineering.96605924aComputational Intelligence.96405224aMachine Learning.9550591 aPedrycz, Witold.eeditor.0(orcid)0000-0002-9335-99301https://orcid.org/0000-0002-9335-99304edt4http://id.loc.gov/vocabulary/relators/edt9700971 aChen, Shyi-Ming.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9847802 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303166841808iPrinted edition:z978303166843208iPrinted edition:z9783031668449 0aStudies in Big Data,x2197-6511 ;v15596872440uhttps://doi.org/10.1007/978-3-031-66842-5  aZDB-2-INR  aZDB-2-SXIT  c63600d63600