Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm

In this paper, we present an application of Multi–Objective Evolutionary Algorithm (MOEA) for generating cluster oriented ensemble classifier. In our recently developed Non–Uniform Layered Cluster Oriented Ensemble Classifier (NULCOEC), the data set is partitioned into a variable number of clusters at different layers. Base classifiers are then trained on the clusters at different layers. The performance of NULCOEC is a function of the vector (layers,clusters) and the research presented in this paper investigates the implication of applying MOEA to generate NULCOEC. Accuracy and diversity of the ensemble classifier is expressed as a function of layers and clusters. A MOEA then searches for the combination of layers and clusters to obtain the non–dominated set of (accuracy,diversity). We have also obtained the results of single objective optimization (i.e. optimizing either accuracy or diversity) and compared them with the results of MOEA. The results show that the MOEA can improve the performance of ensemble classifier