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Machine learning and granular comput...

Pedrycz, Witold.

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Machine learning and granular computing = a synergistic design environment /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and granular computing/ edited by Witold Pedrycz, Shyi-Ming Chen.
Reminder of title:	a synergistic design environment /
other author:	Pedrycz, Witold.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	viii, 352 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-66842-5
ISBN:	9783031668425

Machine learning and granular computing = a synergistic design environment /
Machine learning and granular computinga synergistic design environment /[electronic resource] :edited by Witold Pedrycz, Shyi-Ming Chen. - Cham :Springer Nature Switzerland :2024. - viii, 352 p. :ill. (some col.), digital ;24 cm. - Studies in big data,v. 1552197-6511 ;. - Studies in big data ;v. 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.

ISBN: 9783031668425

Standard No.: 10.1007/978-3-031-66842-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning and granular computing = a synergistic design environment /
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300 $a viii, 352 p. : $b ill. (some col.), digital ; $c 24 cm.
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505 0 $a 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.
520 $a 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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700 1 $a Pedrycz, Witold. $3 899642
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