Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression
Fuzzy rules; Fuzzy systems; Genetic algorithms; Inference engines; Membership functions; Process control; Regression analysis; Functional relationship; Fuzzy inference systems; Human understanding; Hybrid genetic algorithms; Interpretability; Logical interpretation; Optimization tools; Regression; F...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-237692023-05-29T14:51:41Z Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression Wong S.Y. Siah Yap K. Tan C.H. 55812054100 24448864400 55175180600 Fuzzy rules; Fuzzy systems; Genetic algorithms; Inference engines; Membership functions; Process control; Regression analysis; Functional relationship; Fuzzy inference systems; Human understanding; Hybrid genetic algorithms; Interpretability; Logical interpretation; Optimization tools; Regression; Fuzzy inference Regression analysis is one of the most popular methods of estimation or forecasting. For someone who is the non-domain expert to understand how the estimation decision is made, clarity and transparency of the regression model is required to reveal knowledge and information that evaluates the functional relationship between two objects, i.e., the independent and dependent objects the system represents. Hence, this paper presents the hybridization of Genetic Algorithm (GA) and Fuzzy Inference System (FIS)-based computational intelligence systems for tackling data regression problem (hereinafter denoted as GA-FIS-RG). With this regard, GA-FIS-RG first defines the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then GA serves as an optimization tool to construct the best combination of rules in fuzzy inference system. For performance evaluations, we demonstrate the interpretability and applicability of GA-FIS-RG to data regression problems, i.e., the Santa-Fe Series-E and Auto MPG. � 2018 IEEE. Final 2023-05-29T06:51:41Z 2023-05-29T06:51:41Z 2018 Conference Paper 10.1109/SPC.2018.8704148 2-s2.0-85065977407 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065977407&doi=10.1109%2fSPC.2018.8704148&partnerID=40&md5=2d578d6882ba1ead4d3f76ed35938ecb https://irepository.uniten.edu.my/handle/123456789/23769 8704148 60 65 Institute of Electrical and Electronics Engineers Inc. Scopus |
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| description |
Fuzzy rules; Fuzzy systems; Genetic algorithms; Inference engines; Membership functions; Process control; Regression analysis; Functional relationship; Fuzzy inference systems; Human understanding; Hybrid genetic algorithms; Interpretability; Logical interpretation; Optimization tools; Regression; Fuzzy inference |
| author2 |
55812054100 |
| author_facet |
55812054100 Wong S.Y. Siah Yap K. Tan C.H. |
| format |
Conference Paper |
| author |
Wong S.Y. Siah Yap K. Tan C.H. |
| spellingShingle |
Wong S.Y. Siah Yap K. Tan C.H. Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression |
| author_sort |
Wong S.Y. |
| title |
Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression |
| title_short |
Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression |
| title_full |
Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression |
| title_fullStr |
Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression |
| title_full_unstemmed |
Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression |
| title_sort |
hybrid genetic algorithm based fuzzy inference system for data regression |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806425773538541568 |
| score |
13.214268 |