Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression

Shen Yuong Wong, Keem Siah Yap, Chin Hooi Tan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-65
Number of pages6
ISBN (Electronic)9781538663271
DOIs
Publication statusPublished - 30 Apr 2019
Event2018 IEEE Conference on Systems, Process and Control, ICSPC 2018 - Melaka, Malaysia
Duration: 14 Dec 201815 Dec 2018

Publication series

NameProceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018

Conference

Conference2018 IEEE Conference on Systems, Process and Control, ICSPC 2018
CountryMalaysia
CityMelaka
Period14/12/1815/12/18

Fingerprint

Fuzzy inference
Genetic algorithms
Membership functions
Regression analysis
Transparency
Artificial intelligence

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

Wong, S. Y., Yap, K. S., & Tan, C. H. (2019). Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression. In Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018 (pp. 60-65). [8704148] (Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPC.2018.8704148
Wong, Shen Yuong ; Yap, Keem Siah ; Tan, Chin Hooi. / Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression. Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 60-65 (Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018).
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Wong, SY, Yap, KS & Tan, CH 2019, Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression. in Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018., 8704148, Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 60-65, 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018, Melaka, Malaysia, 14/12/18. https://doi.org/10.1109/SPC.2018.8704148

Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression. / Wong, Shen Yuong; Yap, Keem Siah; Tan, Chin Hooi.

Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 60-65 8704148 (Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Wong SY, Yap KS, Tan CH. Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression. In Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 60-65. 8704148. (Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018). https://doi.org/10.1109/SPC.2018.8704148