关键词: 子图, 差异性, 集成学习, 支持向量机

Abstract: For the purpose of reducing redundancy of ensemble learning，a selective classifier ensemble algorithm based on subgraph strategy is proposed.It trains a set of classifiers and constructs a complete undirected graph with weight by using individual and diversity value between individuals，and chooses individuals with larger diversity based on subgraph strategy to construct ensemble members.By choosing Support Vector Machine（SVM） as basis classifier，experimental study is conducted on several data sets and performance of proposed method is compared with that of common Bagging and Adaboost.Experimental results show that the approach obtains better ensemble accuracy.

Key words: subgraph, diversity, ensemble learning, Support Vector Machine（SVM）