A kernel-ensemble bagging support vector machine
Suganthan, P. N.
Date of Issue2012
International Conference on Intelligent Systems Design and Applications (12th : 2012 : Kochi, India)
School of 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.
DRNTU::Engineering::Electrical and electronic engineering
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