Particle swarm optimization based multi-prototype ensembles

This paper proposes and evaluates a Particle Swarm Optimization (PSO) based ensemble classifier. The members of the ensemble are Nearest Prototype Classifiers generated sequentially using PSO and combined by a majority voting mechanism. Two necessary requirements for good performance of an ensemble are accuracy and diversity of error. Accuracy is achieved by PSO minimizing a fitness function representing the error rate as the members are created. The diversity of error is promoted by using a different initialization of PSO each time to create a new member and by adopting decorrelated training where a penalty term is added to the fitness function to penalize particles that make the same errors as previously generated classifiers. Simulation experiments on different classification problems show that the ensemble has better performance than a single classifier and are effective in generating diverse ensemble members.