Designing an ensemble classifier over subspace classifiers using iterative convergence routine

Abstract

There can be multiple classifiers for a given data set. One way to generate multiple classifiers is to use subspaces of the attribute sets. In this paper, we generate subspace classifiers by an iterative convergence routine to build an ensemble classifier. Experimental evaluation covers the cases of both labelled and unlabelled (blind) data separately. We evaluate our approach on many benchmark UC Irvine datasets to assess the robustness of our approach with varying induced noise levels. We explicitly compare and present the utility of the clusterings generated for classification using several diverse clustering dissimilarity metrics. Results show that our ensemble classifier is a more robust classifier in comparison to different multi-class classification approaches.