Constructing Effective SVM Ensembles for Image Classification

摘要：

This paper proposes a novel method for constructing SVM ensembles for achieving improved image classification performance. The use of an SVM ensemble, instead of a single classifier, normally aims at reducing the training complexity and improving the performance. SVM ensembles are constructed by combining a set of base classifiers. While the traditional combination methods primarily consider an individual classifiers performance on the training data, the proposed method also considers its generalization ability. With our proposed method, upon training a set of base classifiers, we estimate an optimal weight for each classifier via solving a quadratic programming problem. Then the weights can be used to combine the base classifiers to form an SVM ensemble. To reduce the classification complexity, we propose an intelligent method for filtering out the weak classifiers to obtain a small subset of the relatively strong classifiers for a simplified SVM ensemble. Experiment results show that our proposed approach outperforms other published methods. When compared to the traditional SVM ensemble method which combines all the base classifiers, the proposed method can develop simplified SVM ensembles which may achieve even better accuracy.

关键词： Support Vector Machine Ensemble Bagging Weighted average

作者: Bin Linghu Bing-Yu Sun

作者单位: Department of AutomationThe University of Science and Technology of ChinaHefei, Anhui, 230031, China The Institute of Intelligence MachinesChinese Academy of ScienceHefei, Anhui, 230031, China

会议类型: 国际会议

会议名称: 2010 Third International Symposium on Knowledge Acquisition and Modeling(第三届知识获取与建模国际研讨会 KAN 2010)

会议地点: 武汉

会议语种:英文

页码: 80-83

在线出版日期: 2010-10-20（万方平台首次上网日期，不代表论文的发表时间）