Evolutionary Classifier and Cluster Selection Approach for Ensemble Classification

Abstract

Ensemble classifiers improve the classification performance by combining several classifiers using a suitable fusion methodology. Many ensemble classifier generation methods have been developed that allowed the training of multiple classifiers on a single dataset. As such random subspace is a common methodology utilized by many state-of-the-art ensemble classifiers that generate random subsamples from the input data and train classifiers on different subsamples. Real-world datasets have randomness and noise in them, therefore not all randomly generated samples are suitable for training. In this article, we propose a novel particle swarm optimization-based approach to optimize the random subspace to generate an ensemble classifier. We first generate a random subspace by incrementally clustering input data and then optimize all generated data clusters. On all optimized data clusters, a set of classifiers is trained and added to the pool. The pool of classifiers is then optimized and an optimized ensemble classifier is generated. The proposed approach is tested on 12 benchmark datasets from the UCI repository and results are compared with current state-of-the-art ensemble classifier approaches. A statistical significance test is also conducted and an analysis is presented.