Study of Selective Ensemble Learning Methods Based on Support Vector Machine

摘要：

Diversity among base classifiers is an important factor for improving in ensemble learning performance. In this paper, we choose support vector machine as base classifier and study four methods of selective ensemble learning which include hillclimbing, ensemble forward sequential selection, ensemble backward sequential selection and clustering selection. To measure the diversity among base classifiers in ensemble learning, the entropy E is used. The experimental results show that different diversity measure impacts on ensemble performance in some extent and first three selective strategies have similar generalization performance. Meanwhile, when using clustering selective strategy, selecting different number of clusters in this experiment also does not impact on the ensemble performance except some dataset.

关键词： Diversity Selective Ensemble GeneralizationError Support Vector Machine

作者: Kai Li Zhibin Liu Yanxia Han

作者单位: School of Mathematics and Computer Hebei University Baoding, Hebei Province, 071002, China School of Mathematics and Computer Hebei University Baoding, Hebei Province,071002, China

会议类型: 国际会议

会议名称: 2010 International Conference on Circuit and Signal Processing(2010年电路与信号处理国际会议 ICCSP 2010)

会议地点: 上海

会议语种:英文

页码: 307-310

在线出版日期: 2010-12-25（万方平台首次上网日期，不代表论文的发表时间）