A kernel-ensemble bagging support vector machine

• المؤلفون الرئيسيون: Suganthan, P. N., Ye, Ren
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2013
• الموضوعات: DRNTU::Engineering::Electrical and electronic engineering
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/106266; http://hdl.handle.net/10220/16622; http://dx.doi.org/10.1109/ISDA.2012.6416648
• المؤسسة: Nanyang Technological University

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.