关键词: 分类器集成, 差异性, 遗传算法, 图着色方法, 信息熵

Abstract: At present, classifier ensemble system has been widely used in various fields of pattern recognition. However, as the number of classifiers increases, the diversity among classifiers can be reduced and redundancy can be generated. Therefore, it is necessary to study how to delete redundant classifiers and reduce the size of the classifier set without ensemble performance degradation. This paper proposes a classifier selection method with hybrid diversity measure. In the method, the paired diversity matrix is transformed into adjacency matrix which is expressed by one or more graphs. Then, using graph coloring method based on genetic algorithm, the classifiers are grouped according to the coloring result. Finally, an evaluation system based on information entropy and non-pairwise diversity measure is presented. According to the weights of each group, a group is selected as the final classifier set. The feasibility of the proposed method is demonstrated by comparing experiments with a variety of ensemble methods on 8 datasets of UCI databases.

Key words: classifier ensemble, diversity, genetic algorithm, graph coloring method, information entropy