An Ensemble Associated Feature Subset Selection for Classification Problems

Tanasanee Phienthrakul

doi:10.1109/ISCBI.2015.18

An Ensemble Associated Feature Subset Selection for Classification Problems

Tanasanee Phienthrakul

Department of Computer Engineering

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

Abstract

Feature subset selection is an important problem in machine learning and data mining. If the suitable features are selected, the results of classification or prediction will be more accurate, while if the unsuitable features are used, the results may have no meaningful. This paper presents a method for feature subset selection that uses the ensemble technique to increase the efficiency of feature selection. Association rule mining is introduced to select the high relationship features. Bagging concept is applied to increase the confidence of selection. The experimental results show the efficiency of the proposed method that outperforms the efficiency of simple association feature subset selection.

Original language	English
Title of host publication	Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015
Editors	Simon Fong, Suash Deb, Leo Willyanto Santoso
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	63-67
Number of pages	5
ISBN (Electronic)	9781467385015
DOIs	https://doi.org/10.1109/ISCBI.2015.18
Publication status	Published - 14 Jan 2016
Event	3rd International Symposium on Computational and Business Intelligence, ISCBI 2015 - Bali, Indonesia Duration: 7 Dec 2015 → 9 Dec 2015

Publication series

Name	Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015

Conference

Conference	3rd International Symposium on Computational and Business Intelligence, ISCBI 2015
Country/Territory	Indonesia
City	Bali
Period	7/12/15 → 9/12/15

Keywords

association rule mining
bagging
ensemble methods
feature subset selection

Access to Document

10.1109/ISCBI.2015.18

Cite this

Phienthrakul, T. (2016). An Ensemble Associated Feature Subset Selection for Classification Problems. In S. Fong, S. Deb, & L. W. Santoso (Eds.), Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015 (pp. 63-67). Article 7383538 (Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCBI.2015.18

Phienthrakul, Tanasanee. / An Ensemble Associated Feature Subset Selection for Classification Problems. Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015. editor / Simon Fong ; Suash Deb ; Leo Willyanto Santoso. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 63-67 (Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015).

@inproceedings{f53d1bd88a784c7681ec22d7afc38b33,

title = "An Ensemble Associated Feature Subset Selection for Classification Problems",

abstract = "Feature subset selection is an important problem in machine learning and data mining. If the suitable features are selected, the results of classification or prediction will be more accurate, while if the unsuitable features are used, the results may have no meaningful. This paper presents a method for feature subset selection that uses the ensemble technique to increase the efficiency of feature selection. Association rule mining is introduced to select the high relationship features. Bagging concept is applied to increase the confidence of selection. The experimental results show the efficiency of the proposed method that outperforms the efficiency of simple association feature subset selection.",

keywords = "association rule mining, bagging, ensemble methods, feature subset selection",

author = "Tanasanee Phienthrakul",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015 ; Conference date: 07-12-2015 Through 09-12-2015",

year = "2016",

month = jan,

day = "14",

doi = "10.1109/ISCBI.2015.18",

language = "English",

series = "Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "63--67",

editor = "Simon Fong and Suash Deb and Santoso, \{Leo Willyanto\}",

booktitle = "Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015",

}

Phienthrakul, T 2016, An Ensemble Associated Feature Subset Selection for Classification Problems. in S Fong, S Deb & LW Santoso (eds), Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015., 7383538, Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015, Institute of Electrical and Electronics Engineers Inc., pp. 63-67, 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015, Bali, Indonesia, 7/12/15. https://doi.org/10.1109/ISCBI.2015.18

An Ensemble Associated Feature Subset Selection for Classification Problems. / Phienthrakul, Tanasanee.
Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015. ed. / Simon Fong; Suash Deb; Leo Willyanto Santoso. Institute of Electrical and Electronics Engineers Inc., 2016. p. 63-67 7383538 (Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015).

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

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AB - Feature subset selection is an important problem in machine learning and data mining. If the suitable features are selected, the results of classification or prediction will be more accurate, while if the unsuitable features are used, the results may have no meaningful. This paper presents a method for feature subset selection that uses the ensemble technique to increase the efficiency of feature selection. Association rule mining is introduced to select the high relationship features. Bagging concept is applied to increase the confidence of selection. The experimental results show the efficiency of the proposed method that outperforms the efficiency of simple association feature subset selection.

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Phienthrakul T. An Ensemble Associated Feature Subset Selection for Classification Problems. In Fong S, Deb S, Santoso LW, editors, Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 63-67. 7383538. (Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015). doi: 10.1109/ISCBI.2015.18

An Ensemble Associated Feature Subset Selection for Classification Problems

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