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020 _a9783031668425
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024 7 _a10.1007/978-3-031-66842-5
_2doi
050 4 _aTA345-345.5
072 7 _aUN
_2bicssc
072 7 _aCOM018000
_2bisacsh
072 7 _aUN
_2thema
245 1 0 _aMachine Learning and Granular Computing: A Synergistic Design Environment
_h[electronic resource] /
_cedited by Witold Pedrycz, Shyi-Ming Chen.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aVIII, 352 p. 116 illus., 103 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Big Data,
_x2197-6511 ;
_v155
505 0 _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.
520 _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.
650 0 _aEngineering
_xData processing.
650 0 _aComputational intelligence.
_964052
650 0 _aMachine learning.
_955059
650 1 4 _aData Engineering.
_966059
650 2 4 _aComputational Intelligence.
_964052
650 2 4 _aMachine Learning.
_955059
700 1 _aPedrycz, Witold.
_eeditor.
_0(orcid)0000-0002-9335-9930
_1https://orcid.org/0000-0002-9335-9930
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_970097
700 1 _aChen, Shyi-Ming.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_984780
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031668418
776 0 8 _iPrinted edition:
_z9783031668432
776 0 8 _iPrinted edition:
_z9783031668449
830 0 _aStudies in Big Data,
_x2197-6511 ;
_v155
_968724
856 4 0 _uhttps://doi.org/10.1007/978-3-031-66842-5
912 _aZDB-2-INR
912 _aZDB-2-SXIT
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