A multi-objective evolutionary algorithm for tuning type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system

Edward Hinojosa Cárdenas; Heloisa A. Camargo

doi:10.1007/978-3-319-66830-7_35

A multi-objective evolutionary algorithm for tuning type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system

Edward Hinojosa Cárdenas, Heloisa A. Camargo

Departamento Académico de Ingeniería de Sistemas e Informática

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

This paper presents a Multi-Objective Evolutionary Algorithm (MOEA) for tuning type-2 fuzzy sets and selecting rules and conditions on Fuzzy Rule-Based Classification Systems (FRBCS). Before the tuning and selection process, the Rule Base is learned by means of a modified Wang-Mendel algorithm that considers type-2 fuzzy sets in the rules antecedents and in the inference mechanism. The Multi-Objective Evolutionary Algorithm used in the tuning process has three objectives. The first objective reflects the accuracy where the correct classification rate of the FRBCS is optimized. The second objective reflects the interpretability of the system regarding complexity, by means of the quantity of rules and is to be minimized through selecting rules from the initial rule base. The third objective also reflects the interpretability as a matter of complexity and models the quantity of conditions in the Rule Base. Finally, we show how the FRBCS tuned by our proposed algorithm can achieve a considerably better classification accuracy and complexity, expressed by the quantity of fuzzy rules and conditions in the RB compared with the FRBCS before the tuning process.

Original language	English
Title of host publication	Advances in Fuzzy Logic and Technology 2017 - Proceedings of
Subtitle of host publication	EUSFLAT-2017 – The 10th Conference of the European Society for Fuzzy Logic and Technology, IWIFSGN’2017 – The 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets
Editors	Janusz Kacprzyk, Eulalia Szmidt, Slawomir Zadrozny, Maciej Krawczak, Maciej Krawczak, Krassimir T. Atanassov
Publisher	Springer Verlag
Pages	389-399
Number of pages	11
ISBN (Print)	9783319668291
DOIs	https://doi.org/10.1007/978-3-319-66830-7_35
State	Published - 2018
Event	10th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2017 and 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2017 - Warsaw, Poland Duration: 11 Sep 2017 → 15 Sep 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	641
ISSN (Print)	2194-5357

Conference

Conference	10th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2017 and 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2017
Country/Territory	Poland
City	Warsaw
Period	11/09/17 → 15/09/17

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG.

Keywords

Fuzzy rule-based classification system
Multi-objective evolutionary algorithm
Type-2 fuzzy sets

Access to Document

10.1007/978-3-319-66830-7_35

Cite this

Cárdenas, E. H., & Camargo, H. A. (2018). A multi-objective evolutionary algorithm for tuning type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system. In J. Kacprzyk, E. Szmidt, S. Zadrozny, M. Krawczak, M. Krawczak, & K. T. Atanassov (Eds.), Advances in Fuzzy Logic and Technology 2017 - Proceedings of: EUSFLAT-2017 – The 10th Conference of the European Society for Fuzzy Logic and Technology, IWIFSGN’2017 – The 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets (pp. 389-399). (Advances in Intelligent Systems and Computing; Vol. 641). Springer Verlag. https://doi.org/10.1007/978-3-319-66830-7_35

Cárdenas, Edward Hinojosa ; Camargo, Heloisa A. / A multi-objective evolutionary algorithm for tuning type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system. Advances in Fuzzy Logic and Technology 2017 - Proceedings of: EUSFLAT-2017 – The 10th Conference of the European Society for Fuzzy Logic and Technology, IWIFSGN’2017 – The 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets. editor / Janusz Kacprzyk ; Eulalia Szmidt ; Slawomir Zadrozny ; Maciej Krawczak ; Maciej Krawczak ; Krassimir T. Atanassov. Springer Verlag, 2018. pp. 389-399 (Advances in Intelligent Systems and Computing).

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abstract = "This paper presents a Multi-Objective Evolutionary Algorithm (MOEA) for tuning type-2 fuzzy sets and selecting rules and conditions on Fuzzy Rule-Based Classification Systems (FRBCS). Before the tuning and selection process, the Rule Base is learned by means of a modified Wang-Mendel algorithm that considers type-2 fuzzy sets in the rules antecedents and in the inference mechanism. The Multi-Objective Evolutionary Algorithm used in the tuning process has three objectives. The first objective reflects the accuracy where the correct classification rate of the FRBCS is optimized. The second objective reflects the interpretability of the system regarding complexity, by means of the quantity of rules and is to be minimized through selecting rules from the initial rule base. The third objective also reflects the interpretability as a matter of complexity and models the quantity of conditions in the Rule Base. Finally, we show how the FRBCS tuned by our proposed algorithm can achieve a considerably better classification accuracy and complexity, expressed by the quantity of fuzzy rules and conditions in the RB compared with the FRBCS before the tuning process.",

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Cárdenas, EH & Camargo, HA 2018, A multi-objective evolutionary algorithm for tuning type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system. in J Kacprzyk, E Szmidt, S Zadrozny, M Krawczak, M Krawczak & KT Atanassov (eds), Advances in Fuzzy Logic and Technology 2017 - Proceedings of: EUSFLAT-2017 – The 10th Conference of the European Society for Fuzzy Logic and Technology, IWIFSGN’2017 – The 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets. Advances in Intelligent Systems and Computing, vol. 641, Springer Verlag, pp. 389-399, 10th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2017 and 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2017, Warsaw, Poland, 11/09/17. https://doi.org/10.1007/978-3-319-66830-7_35

A multi-objective evolutionary algorithm for tuning type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system. / Cárdenas, Edward Hinojosa; Camargo, Heloisa A.
Advances in Fuzzy Logic and Technology 2017 - Proceedings of: EUSFLAT-2017 – The 10th Conference of the European Society for Fuzzy Logic and Technology, IWIFSGN’2017 – The 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets. ed. / Janusz Kacprzyk; Eulalia Szmidt; Slawomir Zadrozny; Maciej Krawczak; Maciej Krawczak; Krassimir T. Atanassov. Springer Verlag, 2018. p. 389-399 (Advances in Intelligent Systems and Computing; Vol. 641).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Camargo, Heloisa A.

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N2 - This paper presents a Multi-Objective Evolutionary Algorithm (MOEA) for tuning type-2 fuzzy sets and selecting rules and conditions on Fuzzy Rule-Based Classification Systems (FRBCS). Before the tuning and selection process, the Rule Base is learned by means of a modified Wang-Mendel algorithm that considers type-2 fuzzy sets in the rules antecedents and in the inference mechanism. The Multi-Objective Evolutionary Algorithm used in the tuning process has three objectives. The first objective reflects the accuracy where the correct classification rate of the FRBCS is optimized. The second objective reflects the interpretability of the system regarding complexity, by means of the quantity of rules and is to be minimized through selecting rules from the initial rule base. The third objective also reflects the interpretability as a matter of complexity and models the quantity of conditions in the Rule Base. Finally, we show how the FRBCS tuned by our proposed algorithm can achieve a considerably better classification accuracy and complexity, expressed by the quantity of fuzzy rules and conditions in the RB compared with the FRBCS before the tuning process.

AB - This paper presents a Multi-Objective Evolutionary Algorithm (MOEA) for tuning type-2 fuzzy sets and selecting rules and conditions on Fuzzy Rule-Based Classification Systems (FRBCS). Before the tuning and selection process, the Rule Base is learned by means of a modified Wang-Mendel algorithm that considers type-2 fuzzy sets in the rules antecedents and in the inference mechanism. The Multi-Objective Evolutionary Algorithm used in the tuning process has three objectives. The first objective reflects the accuracy where the correct classification rate of the FRBCS is optimized. The second objective reflects the interpretability of the system regarding complexity, by means of the quantity of rules and is to be minimized through selecting rules from the initial rule base. The third objective also reflects the interpretability as a matter of complexity and models the quantity of conditions in the Rule Base. Finally, we show how the FRBCS tuned by our proposed algorithm can achieve a considerably better classification accuracy and complexity, expressed by the quantity of fuzzy rules and conditions in the RB compared with the FRBCS before the tuning process.

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KW - Multi-objective evolutionary algorithm

KW - Type-2 fuzzy sets

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Cárdenas EH, Camargo HA. A multi-objective evolutionary algorithm for tuning type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system. In Kacprzyk J, Szmidt E, Zadrozny S, Krawczak M, Krawczak M, Atanassov KT, editors, Advances in Fuzzy Logic and Technology 2017 - Proceedings of: EUSFLAT-2017 – The 10th Conference of the European Society for Fuzzy Logic and Technology, IWIFSGN’2017 – The 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets. Springer Verlag. 2018. p. 389-399. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-66830-7_35

A multi-objective evolutionary algorithm for tuning type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system

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Keywords

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