Evaluation of particle swarm optimization effectiveness in classification

Particle Swarm Optimization (PSO) is a heuristic optimization technique showing relationship with Evolutionary Algorithms and strongly based on the concept of swarm. It is used in this paper to face the problem of classification of instances in multiclass databases. Only a few papers exist in literature in which PSO is tested on this problem and there are no papers showing a thorough comparison for it against a wide set of techniques typically used in the field. Therefore in this paper PSO performance is compared on nine typical test databases against those of nine classification techniques widely used for classification purposes. PSO is used to find the optimal positions of class centroids in the database attribute space, via the examples contained in the training set. Performance of a run, instead, is computed as the percentage of instances of testing set which are incorrectly classified by the best individual achieved in the run. Results show the effectiveness of PSO, which turns out to be the best on three out of the nine challenged problems.