A hybrid model of fuzzy artmap and the genetic algorithm for data classification

Manjeevan Seera; Wei Shiung Liew; Chu Kiong Loo

doi:10.1007/978-3-319-26535-3_40

A hybrid model of fuzzy artmap and the genetic algorithm for data classification

Manjeevan Seera, Wei Shiung Liew, Chu Kiong Loo

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

Abstract

A framework for optimizing Fuzzy ARTMAP (FAM) neural networks using Genetic Algorithms (GAs) is proposed in this paper. A number of variables were identified for optimization, which include the presentation order of training data during the learning step, the feature subset selection of the training data, and the internal parameters of the FAM such as baseline vigilance and match tracking. A single configuration of all three variables were encoded as a chromosome string and evaluated by creating and training the FAM according to the variables. The fitness of the chromosome is determined by the final classification accuracy of the FAM. Evaluation on benchmark data sets are conducted with the results compared with literature. Experimental results indicate the effectiveness of the proposed framework in undertaking data classification tasks.

Original language	English
Title of host publication	Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
Editors	Weng Kin Lai, Qingshan Liu, Tingwen Huang, Sabri Arik
Publisher	Springer
Pages	346-353
Number of pages	8
ISBN (Print)	9783319265346
DOIs	https://doi.org/10.1007/978-3-319-26535-3_40
Publication status	Published - 2015
Externally published	Yes
Event	International Conference on Neural Information Processing 2015 - Istanbul, Türkiye Duration: 9 Nov 2015 → 12 Nov 2015 Conference number: 22nd https://web.archive.org/web/20151210114427/http://www.iconip2015.org/ https://link.springer.com/chapter/10.1007%2F978-3-319-26535-3_5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9490
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Neural Information Processing 2015
Abbreviated title	ICONIP 2015
Country/Territory	Türkiye
City	Istanbul
Period	9/11/15 → 12/11/15
Internet address	https://web.archive.org/web/20151210114427/http://www.iconip2015.org/ https://link.springer.com/chapter/10.1007%2F978-3-319-26535-3_5

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10.1007/978-3-319-26535-3_40

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Seera, M., Liew, W. S., & Loo, C. K. (2015). A hybrid model of fuzzy artmap and the genetic algorithm for data classification. In W. K. Lai, Q. Liu, T. Huang, & S. Arik (Eds.), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings (pp. 346-353). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9490). Springer. https://doi.org/10.1007/978-3-319-26535-3_40

Seera, Manjeevan ; Liew, Wei Shiung ; Loo, Chu Kiong. / A hybrid model of fuzzy artmap and the genetic algorithm for data classification. Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. editor / Weng Kin Lai ; Qingshan Liu ; Tingwen Huang ; Sabri Arik. Springer, 2015. pp. 346-353 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A hybrid model of fuzzy artmap and the genetic algorithm for data classification",

abstract = "A framework for optimizing Fuzzy ARTMAP (FAM) neural networks using Genetic Algorithms (GAs) is proposed in this paper. A number of variables were identified for optimization, which include the presentation order of training data during the learning step, the feature subset selection of the training data, and the internal parameters of the FAM such as baseline vigilance and match tracking. A single configuration of all three variables were encoded as a chromosome string and evaluated by creating and training the FAM according to the variables. The fitness of the chromosome is determined by the final classification accuracy of the FAM. Evaluation on benchmark data sets are conducted with the results compared with literature. Experimental results indicate the effectiveness of the proposed framework in undertaking data classification tasks.",

author = "Manjeevan Seera and Liew, \{Wei Shiung\} and Loo, \{Chu Kiong\}",

note = "Funding Information: This research is supported by Collaborative Research in Engineering, Science Technology (CREST) Grant P05C2-14 and University of Malaya Grant UM.C/625/1/HIR/MOHE/FCSIT/10. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; International Conference on Neural Information Processing 2015, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

year = "2015",

doi = "10.1007/978-3-319-26535-3\_40",

language = "English",

isbn = "9783319265346",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",

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Seera, M, Liew, WS & Loo, CK 2015, A hybrid model of fuzzy artmap and the genetic algorithm for data classification. in WK Lai, Q Liu, T Huang & S Arik (eds), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9490, Springer, pp. 346-353, International Conference on Neural Information Processing 2015, Istanbul, Türkiye, 9/11/15. https://doi.org/10.1007/978-3-319-26535-3_40

A hybrid model of fuzzy artmap and the genetic algorithm for data classification. / Seera, Manjeevan; Liew, Wei Shiung; Loo, Chu Kiong.
Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. ed. / Weng Kin Lai; Qingshan Liu; Tingwen Huang; Sabri Arik. Springer, 2015. p. 346-353 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9490).

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

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AB - A framework for optimizing Fuzzy ARTMAP (FAM) neural networks using Genetic Algorithms (GAs) is proposed in this paper. A number of variables were identified for optimization, which include the presentation order of training data during the learning step, the feature subset selection of the training data, and the internal parameters of the FAM such as baseline vigilance and match tracking. A single configuration of all three variables were encoded as a chromosome string and evaluated by creating and training the FAM according to the variables. The fitness of the chromosome is determined by the final classification accuracy of the FAM. Evaluation on benchmark data sets are conducted with the results compared with literature. Experimental results indicate the effectiveness of the proposed framework in undertaking data classification tasks.

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Seera M, Liew WS, Loo CK. A hybrid model of fuzzy artmap and the genetic algorithm for data classification. In Lai WK, Liu Q, Huang T, Arik S, editors, Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. Springer. 2015. p. 346-353. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-26535-3_40

A hybrid model of fuzzy artmap and the genetic algorithm for data classification

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