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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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Main Authors: Wong S.Y., Siah Yap K., Tan C.H.
Other Authors: 55812054100
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling 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
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
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 13.211869

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