Non–uniform layered clustering for ensemble classifier generation and optimality

In this paper we present an approach to generate ensemble of classifiers using non–uniform layered clustering. In the proposed approach the dataset is partitioned into variable number of clusters at different layers. A set of base classifiers is trained on the clusters at different layers. The decision on a pattern at each layer is obtained from the classifier trained on the nearest cluster and the decisions from the different layers are fused using majority voting to obtain the final verdict. The proposed approach provides a mechanism to obtain the optimal number of layers and clusters using Genetic Algorithm. Clustering identifies difficult–to–classify patterns and layered non–uniform clustering approach brings in diversity among the base classifiers at different layers. The proposed method performs relatively better than the other state–of–art ensemble classifier generation methods as evidenced from the experimental results.