A kernel-ensemble bagging support vector machine

This paper proposes a kernel-ensemble bagging SVM classifier for binary class classification. The classifier is advantageous over bagging SVM classifiers because it has a two-phase grid search module, a proposed parameter randomization module and a proposed ranking module. The novel modules enhance...

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Main Authors:	Suganthan, P. N., Ye, Ren
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	DRNTU::Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/106266 http://hdl.handle.net/10220/16622 http://dx.doi.org/10.1109/ISDA.2012.6416648
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-106266
record_format	dspace
spelling	sg-ntu-dr.10356-1062662019-12-06T22:07:42Z A kernel-ensemble bagging support vector machine Suganthan, P. N. Ye, Ren School of Electrical and Electronic Engineering International Conference on Intelligent Systems Design and Applications (12th : 2012 : Kochi, India) DRNTU::Engineering::Electrical and electronic engineering This paper proposes a kernel-ensemble bagging SVM classifier for binary class classification. The classifier is advantageous over bagging SVM classifiers because it has a two-phase grid search module, a proposed parameter randomization module and a proposed ranking module. The novel modules enhance the diversity thus improve the performance of the proposed SVM classifier. Six UCI datasets are used to evaluate the proposed kernel-ensemble bagging SVM. The result show that the proposed SVM classifier outperforms the single kernel bagging SVM classifiers. 2013-10-18T07:06:33Z 2019-12-06T22:07:42Z 2013-10-18T07:06:33Z 2019-12-06T22:07:42Z 2012 2012 Conference Paper Ye, R., & Suganthan, P.N. (2012). A kernel-ensemble bagging support vector machine. 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), pp.847-852. https://hdl.handle.net/10356/106266 http://hdl.handle.net/10220/16622 http://dx.doi.org/10.1109/ISDA.2012.6416648 en © 2012 IEEE
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering Suganthan, P. N. Ye, Ren A kernel-ensemble bagging support vector machine
description	This paper proposes a kernel-ensemble bagging SVM classifier for binary class classification. The classifier is advantageous over bagging SVM classifiers because it has a two-phase grid search module, a proposed parameter randomization module and a proposed ranking module. The novel modules enhance the diversity thus improve the performance of the proposed SVM classifier. Six UCI datasets are used to evaluate the proposed kernel-ensemble bagging SVM. The result show that the proposed SVM classifier outperforms the single kernel bagging SVM classifiers.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Suganthan, P. N. Ye, Ren
format	Conference or Workshop Item
author	Suganthan, P. N. Ye, Ren
author_sort	Suganthan, P. N.
title	A kernel-ensemble bagging support vector machine
title_short	A kernel-ensemble bagging support vector machine
title_full	A kernel-ensemble bagging support vector machine
title_fullStr	A kernel-ensemble bagging support vector machine
title_full_unstemmed	A kernel-ensemble bagging support vector machine
title_sort	kernel-ensemble bagging support vector machine
publishDate	2013
url	https://hdl.handle.net/10356/106266 http://hdl.handle.net/10220/16622 http://dx.doi.org/10.1109/ISDA.2012.6416648
_version_	1681041615682011136

A kernel-ensemble bagging support vector machine

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