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Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression

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dc.contributor.authorWong S.Y.en_US
dc.contributor.authorSiah Yap K.en_US
dc.contributor.authorTan C.H.en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55175180600en_US
dc.date.accessioned2023-05-29T06:51:41Z
dc.date.available2023-05-29T06:51:41Z
dc.date.issued2018
dc.descriptionFuzzy 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 inferenceen_US
dc.description.abstractRegression 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8704148
dc.identifier.doi10.1109/SPC.2018.8704148
dc.identifier.epage65
dc.identifier.scopus2-s2.0-85065977407
dc.identifier.spage60
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85065977407&doi=10.1109%2fSPC.2018.8704148&partnerID=40&md5=2d578d6882ba1ead4d3f76ed35938ecb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23769
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018
dc.titleHybrid Genetic Algorithm based Fuzzy Inference System for Data Regressionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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