Design and Stability Analysis of Multi-Objective Ensemble Classifiers

In this paper a novel multi-objective optimization technique based on Inclined Planes Optimization algorithm (called MOIPO), is used to design an ensemble classifier. Diversity, ensemble size, and error rate are three objectives which are considered along designing the proposed ensemble classifier. The performance of designed ensemble classifier is tested on different kinds of benchmarks with nonlinear, overlapping class boundaries, and different feature space dimensions. Extensive experimental and comparative results on these data sets provided to show the performance of the proposed method, are better than ensemble designed by Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Another important aspect of this article is stability analysis of designed ensemble classifier. In fact, for the first time, the stability of a heuristic ensemble classifier is analyzed by using statistical method. For this aim, three regression models are investigated by applying F-test to find better model in each case. Due to the results of stability analysis, quadratic model is the best model for two datasets as representative of simple data and overlapped data.