A novel ensemble classifier approach using weak classifier learning on overlapping clusters

This paper presents a novel approach for creating and training of an ensemble classifier. The approach is based on creating atomic and non-atomic clusters at different levels, training of weak classifiers on overlapping clusters and fusion of their decisions. The subsets of data are obtained by clustering of original training data sets into multiple partitions. As each partition represents highly correlated patterns from different classes, the proposed approach trains weak classifiers on difficult-to-classify patterns and combines the decision at various levels. The approach is tested on six benchmark datasets from UCI machine learning repository. The results show that the proposed approach achieves better classification accuracy than the existing approaches.