A Boundary based Classifier Combination Method
In this paper, a new classifier combination method is proposed for two-class problems. The boundaries of the classes are extracted directly from the given training set, and a set of linear combination rules are defined based on each sample on the class boundaries. The new approach is tested on two large public datasets, and the experimental results show its good performances. Comparing with combination methods such as linear combination, voting, decision templates, our method has higher classification accuracy; comparing with the k-NN rule, its computational complexity is much lower.
Pattern Recognition Information fusion Classifier combination
Ming Liu Kunlun Li Rui Zhao
College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3777-3782
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）