Stochastic and non-stochastic feature selection

Antonio J. Tallón-Ballesteros; Luís Correia; Sung Bae Cho

doi:10.1007/978-3-319-68935-7_64

Stochastic and non-stochastic feature selection

Antonio J. Tallón-Ballesteros, Luís Correia, Sung Bae Cho

College of Computing

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

6 Citations (Scopus)

Abstract

Feature selection has been applied in several areas of science and engineering for a long time. This kind of pre-processing is almost mandatory in problems with huge amounts of features which requires a very high computational cost and also may be handicapped very frequently with more than two classes and lot of instances. The general taxonomy clearly divides the approaches into two groups such as filters and wrappers. This paper introduces a methodology to refine the feature subset with an additional feature selection approach. It reviews the possibilities and deepens into a new class of algorithms based on a refinement of an initial search with another method. We apply sequentially an approximate procedure and an exact procedure. The research is supported by empirical results and some guidelines are drawn as conclusions of this paper.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings
Editors	Hujun Yin, Minling Zhang, Yimin Wen, Guoyong Cai, Tianlong Gu, Antonio J. Tallon-Ballesteros, Junping Du, Yang Gao, Songcan Chen
Publisher	Springer Verlag
Pages	592-598
Number of pages	7
ISBN (Print)	9783319689340
DOIs	https://doi.org/10.1007/978-3-319-68935-7_64
Publication status	Published - 2017
Event	18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017 - Guilin, China Duration: 2017 Oct 30 → 2017 Nov 1

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10585 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017
Country/Territory	China
City	Guilin
Period	17/10/30 → 17/11/1

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-68935-7_64

Cite this

Tallón-Ballesteros, A. J., Correia, L., & Cho, S. B. (2017). Stochastic and non-stochastic feature selection. In H. Yin, M. Zhang, Y. Wen, G. Cai, T. Gu, A. J. Tallon-Ballesteros, J. Du, Y. Gao, & S. Chen (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings (pp. 592-598). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10585 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68935-7_64

Tallón-Ballesteros, Antonio J. ; Correia, Luís ; Cho, Sung Bae. / Stochastic and non-stochastic feature selection. Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings. editor / Hujun Yin ; Minling Zhang ; Yimin Wen ; Guoyong Cai ; Tianlong Gu ; Antonio J. Tallon-Ballesteros ; Junping Du ; Yang Gao ; Songcan Chen. Springer Verlag, 2017. pp. 592-598 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Feature selection has been applied in several areas of science and engineering for a long time. This kind of pre-processing is almost mandatory in problems with huge amounts of features which requires a very high computational cost and also may be handicapped very frequently with more than two classes and lot of instances. The general taxonomy clearly divides the approaches into two groups such as filters and wrappers. This paper introduces a methodology to refine the feature subset with an additional feature selection approach. It reviews the possibilities and deepens into a new class of algorithms based on a refinement of an initial search with another method. We apply sequentially an approximate procedure and an exact procedure. The research is supported by empirical results and some guidelines are drawn as conclusions of this paper.",

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Tallón-Ballesteros, AJ, Correia, L & Cho, SB 2017, Stochastic and non-stochastic feature selection. in H Yin, M Zhang, Y Wen, G Cai, T Gu, AJ Tallon-Ballesteros, J Du, Y Gao & S Chen (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10585 LNCS, Springer Verlag, pp. 592-598, 18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017, Guilin, China, 17/10/30. https://doi.org/10.1007/978-3-319-68935-7_64

Stochastic and non-stochastic feature selection. / Tallón-Ballesteros, Antonio J.; Correia, Luís; Cho, Sung Bae.
Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings. ed. / Hujun Yin; Minling Zhang; Yimin Wen; Guoyong Cai; Tianlong Gu; Antonio J. Tallon-Ballesteros; Junping Du; Yang Gao; Songcan Chen. Springer Verlag, 2017. p. 592-598 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10585 LNCS).

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

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T1 - Stochastic and non-stochastic feature selection

AU - Tallón-Ballesteros, Antonio J.

AU - Correia, Luís

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PY - 2017

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N2 - Feature selection has been applied in several areas of science and engineering for a long time. This kind of pre-processing is almost mandatory in problems with huge amounts of features which requires a very high computational cost and also may be handicapped very frequently with more than two classes and lot of instances. The general taxonomy clearly divides the approaches into two groups such as filters and wrappers. This paper introduces a methodology to refine the feature subset with an additional feature selection approach. It reviews the possibilities and deepens into a new class of algorithms based on a refinement of an initial search with another method. We apply sequentially an approximate procedure and an exact procedure. The research is supported by empirical results and some guidelines are drawn as conclusions of this paper.

AB - Feature selection has been applied in several areas of science and engineering for a long time. This kind of pre-processing is almost mandatory in problems with huge amounts of features which requires a very high computational cost and also may be handicapped very frequently with more than two classes and lot of instances. The general taxonomy clearly divides the approaches into two groups such as filters and wrappers. This paper introduces a methodology to refine the feature subset with an additional feature selection approach. It reviews the possibilities and deepens into a new class of algorithms based on a refinement of an initial search with another method. We apply sequentially an approximate procedure and an exact procedure. The research is supported by empirical results and some guidelines are drawn as conclusions of this paper.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Gu, Tianlong

A2 - Tallon-Ballesteros, Antonio J.

A2 - Du, Junping

A2 - Gao, Yang

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PB - Springer Verlag

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Y2 - 30 October 2017 through 1 November 2017

ER -

Tallón-Ballesteros AJ, Correia L, Cho SB. Stochastic and non-stochastic feature selection. In Yin H, Zhang M, Wen Y, Cai G, Gu T, Tallon-Ballesteros AJ, Du J, Gao Y, Chen S, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings. Springer Verlag. 2017. p. 592-598. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-68935-7_64

Stochastic and non-stochastic feature selection

Abstract

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