Multiple Classifiers Fusion Based on Weighted Evidence Combination

摘要：

Multiple Classifiers Fusion is to utilize distinguished classifiers to resolve the same classification problem as a single classifier does, which can improve performance and generalization capability. In this paper, a new method of multiple classifiers fusion based on weighted evidence combination is proposed. Independent member classifiers are designed based on heterogeneous features by utilizing Artificial Neural Network (ANN). The Basic Probability Assignments (BPA or mass function) are generated based on member classifiers’ outputs corresponding to a given test sample. The weights of each member classifier are defined based on their respective class-wise classification performance on training dataset. Based on weighted evidence combination, classification results of the fused classifier can be obtained, which is better than those derived based on Dempster rule of combination without weights. The experimental results provided in this paper verify the rationality and efficacy of the method proposed.

关键词： Multiple Classifiers Combination Evidence Theory Basic Probability Assignment Artificial Neural Network (ANN)

作者: Deqiang Han ChongZhao Han Yi Yang

作者单位: Institute of Integrated Automation Xian Jiaotong University 710049, Xian, Shaanxi, China

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）