Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation

Tianhua Chen, Qiang Shen, Pan Su, Changjing Shang

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

1 Citation (Scopus)

Abstract

The use of fuzzy quantifiers to modify the fuzzy linguistic terms in fuzzy models helps build fuzzy systems in a more natural way, by capturing finer pieces of information embedded in the training data. This paper presents a practical approach for the acquisition of fuzzy production rules with quantifiers, based on a class-dependent simultaneous rule learning strategy where each class is associated with a subset of descriptive rules. It is implemented by particle swam optimisation. The performance of the learned fuzzy rules with and without fuzzy quantifiers is evaluated on various UCI benchmark data sets, in comparison to popular alternative rule based learning classifiers. Experimental results demonstrate that rule bases generated by the proposed approach indeed boost classification performance as compared to those involving no fuzzy quantifiers, with at least competitive performance to the alternative learning classifiers.
LanguageEnglish
Title of host publicationProceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781467374286
DOIs
Publication statusPublished - 30 Nov 2015
Externally publishedYes

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Fuzzy rules
Particle swarm optimization (PSO)
Classifiers
Fuzzy systems
Linguistics

Cite this

Chen, T., Shen, Q., Su, P., & Shang, C. (2015). Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation. In Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015) Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2015.7337883
Chen, Tianhua ; Shen, Qiang ; Su, Pan ; Shang, Changjing. / Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation. Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015). Institute of Electrical and Electronics Engineers Inc., 2015.
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title = "Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation",
abstract = "The use of fuzzy quantifiers to modify the fuzzy linguistic terms in fuzzy models helps build fuzzy systems in a more natural way, by capturing finer pieces of information embedded in the training data. This paper presents a practical approach for the acquisition of fuzzy production rules with quantifiers, based on a class-dependent simultaneous rule learning strategy where each class is associated with a subset of descriptive rules. It is implemented by particle swam optimisation. The performance of the learned fuzzy rules with and without fuzzy quantifiers is evaluated on various UCI benchmark data sets, in comparison to popular alternative rule based learning classifiers. Experimental results demonstrate that rule bases generated by the proposed approach indeed boost classification performance as compared to those involving no fuzzy quantifiers, with at least competitive performance to the alternative learning classifiers.",
keywords = "Pragmatics, Optimization, Encoding, Particle swarm optimization, Fuzzy sets, Training, Arrays",
author = "Tianhua Chen and Qiang Shen and Pan Su and Changjing Shang",
year = "2015",
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doi = "10.1109/FUZZ-IEEE.2015.7337883",
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booktitle = "Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015)",
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Chen, T, Shen, Q, Su, P & Shang, C 2015, Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation. in Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/FUZZ-IEEE.2015.7337883

Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation. / Chen, Tianhua; Shen, Qiang; Su, Pan ; Shang, Changjing.

Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015). Institute of Electrical and Electronics Engineers Inc., 2015.

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

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N2 - The use of fuzzy quantifiers to modify the fuzzy linguistic terms in fuzzy models helps build fuzzy systems in a more natural way, by capturing finer pieces of information embedded in the training data. This paper presents a practical approach for the acquisition of fuzzy production rules with quantifiers, based on a class-dependent simultaneous rule learning strategy where each class is associated with a subset of descriptive rules. It is implemented by particle swam optimisation. The performance of the learned fuzzy rules with and without fuzzy quantifiers is evaluated on various UCI benchmark data sets, in comparison to popular alternative rule based learning classifiers. Experimental results demonstrate that rule bases generated by the proposed approach indeed boost classification performance as compared to those involving no fuzzy quantifiers, with at least competitive performance to the alternative learning classifiers.

AB - The use of fuzzy quantifiers to modify the fuzzy linguistic terms in fuzzy models helps build fuzzy systems in a more natural way, by capturing finer pieces of information embedded in the training data. This paper presents a practical approach for the acquisition of fuzzy production rules with quantifiers, based on a class-dependent simultaneous rule learning strategy where each class is associated with a subset of descriptive rules. It is implemented by particle swam optimisation. The performance of the learned fuzzy rules with and without fuzzy quantifiers is evaluated on various UCI benchmark data sets, in comparison to popular alternative rule based learning classifiers. Experimental results demonstrate that rule bases generated by the proposed approach indeed boost classification performance as compared to those involving no fuzzy quantifiers, with at least competitive performance to the alternative learning classifiers.

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KW - Optimization

KW - Encoding

KW - Particle swarm optimization

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Chen T, Shen Q, Su P, Shang C. Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation. In Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015). Institute of Electrical and Electronics Engineers Inc. 2015 https://doi.org/10.1109/FUZZ-IEEE.2015.7337883