Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm

In this paper, we propose a novel cluster oriented ensemble classifier generation method and a Genetic Algorithm based approach to optimize the parameters. In the proposed method the data set is partitioned into a variable number of clusters at different layers. Base classifiers are trained on the clusters at different layers. Due to the variability of the number of clusters at different layers, the cluster compositions in one layer are different from that in another layer. Due to this difference in cluster contents, the base classifiers trained at different layers are diverse among each other. A test pattern is classified by the base classifier of the nearest cluster at each layer and the decisions from different layers are fused using majority voting. The accuracy of the proposed method depends on the number of layers and the number of clusters at the corresponding layer. A Genetic Algorithm based search is incorporated to obtain the optimal number of layers and clusters. The Genetic Algorithm is evaluated under three different objective functions: optimizing (i) accuracy, (ii) diversity, and (iii) accuracy × diversity. We have conducted a number of experiments to evaluate the effectiveness of the different objective functions.