Genetic feature selection for optimal functional link artificial neural network in classification

Satchidananda Dehuri; Bijan Bihari Mishra; Sung Bae Cho

doi:10.1007/978-3-540-88906-9_20

Genetic feature selection for optimal functional link artificial neural network in classification

Satchidananda Dehuri, Bijan Bihari Mishra, Sung Bae Cho

Department of Computer Science

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

29 Citations (Scopus)

Abstract

This paper proposed a hybrid functional link artificial neural network (HFLANN) embedded with an optimization of input features for solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded selected features, HFLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer neural networks. An extensive simulation studies has been carried out to illustrate the effectiveness of this method over to its rival functional link artificial neural network (FLANN) and radial basis function (RBF) neural network.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings
Publisher	Springer Verlag
Pages	156-163
Number of pages	8
ISBN (Print)	3540889051, 9783540889052
DOIs	https://doi.org/10.1007/978-3-540-88906-9_20
Publication status	Published - 2008
Event	9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008 - Daejeon, Korea, Republic of Duration: 2008 Nov 2 → 2008 Nov 5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5326 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008
Country/Territory	Korea, Republic of
City	Daejeon
Period	08/11/2 → 08/11/5

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-88906-9_20

Cite this

Dehuri, S., Mishra, B. B., & Cho, S. B. (2008). Genetic feature selection for optimal functional link artificial neural network in classification. In Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings (pp. 156-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5326 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-88906-9_20

Dehuri, Satchidananda ; Mishra, Bijan Bihari ; Cho, Sung Bae. / Genetic feature selection for optimal functional link artificial neural network in classification. Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings. Springer Verlag, 2008. pp. 156-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Genetic feature selection for optimal functional link artificial neural network in classification",

abstract = "This paper proposed a hybrid functional link artificial neural network (HFLANN) embedded with an optimization of input features for solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded selected features, HFLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer neural networks. An extensive simulation studies has been carried out to illustrate the effectiveness of this method over to its rival functional link artificial neural network (FLANN) and radial basis function (RBF) neural network.",

author = "Satchidananda Dehuri and Mishra, {Bijan Bihari} and Cho, {Sung Bae}",

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Dehuri, S, Mishra, BB & Cho, SB 2008, Genetic feature selection for optimal functional link artificial neural network in classification. in Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5326 LNCS, Springer Verlag, pp. 156-163, 9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008, Daejeon, Korea, Republic of, 08/11/2. https://doi.org/10.1007/978-3-540-88906-9_20

Genetic feature selection for optimal functional link artificial neural network in classification. / Dehuri, Satchidananda; Mishra, Bijan Bihari; Cho, Sung Bae.

Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings. Springer Verlag, 2008. p. 156-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5326 LNCS).

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

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AB - This paper proposed a hybrid functional link artificial neural network (HFLANN) embedded with an optimization of input features for solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded selected features, HFLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer neural networks. An extensive simulation studies has been carried out to illustrate the effectiveness of this method over to its rival functional link artificial neural network (FLANN) and radial basis function (RBF) neural network.

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Dehuri S, Mishra BB, Cho SB. Genetic feature selection for optimal functional link artificial neural network in classification. In Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings. Springer Verlag. 2008. p. 156-163. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-88906-9_20

Genetic feature selection for optimal functional link artificial neural network in classification

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