A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems

Liang Yu Chen; Po Ming Lee; Tzu-Chien Hsiao

doi:10.1109/CEC.2015.7257256

A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems

Liang Yu Chen, Po Ming Lee, Tzu-Chien Hsiao

Department of Computer Science

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

1 Scopus citations

Abstract

During the last decade, the extraction and reuse of building blocks of knowledge for the learning process of Extended Classifier System (XCS) in Multiplexer (MUX) problem domain have been demonstrate feasible by using Code Fragment (CF) (i.e. A tree-based structure ordinarily used in the field of Genetic Programming (GP)) as the representation of classifier conditions (the resulting system was called XCSCFC). However, the use of the tree-based structure may lead to the bloating problem and increase in time complexity when the tree grows deep. Therefore, we proposed a novel representation of classifier conditions for the XCS, named Sensory Tag (ST). The XCS with the ST as the input representation is called XCSSTC. The experiments of the proposed method were conducted in the MUX problem domain. The results indicate that the XCSSTC is capable of reusing building blocks of knowledge in the MUX problems. The current study also discussed about two different aspects of reusing of building blocks of knowledge. Specifically, we proposed the 'attribution selection' part and the 'logical relation between the attributes' part.

Original language	English
Title of host publication	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2953-2960
Number of pages	8
ISBN (Electronic)	9781479974924
DOIs	https://doi.org/10.1109/CEC.2015.7257256
State	Published - 10 Sep 2015
Event	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 25 May 2015 → 28 May 2015

Publication series

Name	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Conference

Conference	IEEE Congress on Evolutionary Computation, CEC 2015
Country/Territory	Japan
City	Sendai
Period	25/05/15 → 28/05/15

Keywords

Building Blocks
Extended Classifier System (XCS)
Hash table
Pattern Recognition
Scalability
Sensory Tag

Access to Document

10.1109/CEC.2015.7257256

Cite this

Chen, L. Y., Lee, P. M., & Hsiao, T.-C. (2015). A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2953-2960). Article 7257256 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2015.7257256

Chen, Liang Yu ; Lee, Po Ming ; Hsiao, Tzu-Chien. / A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems. 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2953-2960 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

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title = "A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems",

abstract = "During the last decade, the extraction and reuse of building blocks of knowledge for the learning process of Extended Classifier System (XCS) in Multiplexer (MUX) problem domain have been demonstrate feasible by using Code Fragment (CF) (i.e. A tree-based structure ordinarily used in the field of Genetic Programming (GP)) as the representation of classifier conditions (the resulting system was called XCSCFC). However, the use of the tree-based structure may lead to the bloating problem and increase in time complexity when the tree grows deep. Therefore, we proposed a novel representation of classifier conditions for the XCS, named Sensory Tag (ST). The XCS with the ST as the input representation is called XCSSTC. The experiments of the proposed method were conducted in the MUX problem domain. The results indicate that the XCSSTC is capable of reusing building blocks of knowledge in the MUX problems. The current study also discussed about two different aspects of reusing of building blocks of knowledge. Specifically, we proposed the 'attribution selection' part and the 'logical relation between the attributes' part.",

keywords = "Building Blocks, Extended Classifier System (XCS), Hash table, Pattern Recognition, Scalability, Sensory Tag",

author = "Chen, {Liang Yu} and Lee, {Po Ming} and Tzu-Chien Hsiao",

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Chen, LY, Lee, PM & Hsiao, T-C 2015, A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7257256, 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2953-2960, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 25/05/15. https://doi.org/10.1109/CEC.2015.7257256

A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems. / Chen, Liang Yu; Lee, Po Ming; Hsiao, Tzu-Chien.
2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2953-2960 7257256 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

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

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Chen LY, Lee PM, Hsiao TC. A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2953-2960. 7257256. (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). doi: 10.1109/CEC.2015.7257256

A sensor tagging approach for reusing building blocks of knowledge in Learning Classifier Systems

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Keywords

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