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: | , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | 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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| 總結: | 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. |
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