Learning Automata Based Classifier

Abstract

In this paper a new classifier has been designed based on the learning automata. This classifier can efficiently approximate the decision hyperplanes in the feature space without need to know the class distributions and the a priori probabilities. The performance of the proposed classifier has been tested on different kinds of benchmarks with nonlinear, overlapping class boundaries and different feature space dimensions. Extensive experimental results on these data sets are provided to show that the performance of the proposed classifier is comparable to, sometimes better than multi-layer perceptron, k-nearest neighbor classifier, genetic classifier, and particle swarm classifier. Also the comparative results are provided to show the effectiveness of the proposed method in comparison to similar researches. Furthermore the effect of the number of training points on the performance of the designed classifier is investigated. It is found that as the number of training data increases, the performance of the classifier tends to the performance of Bayes classifier which is an optimal one. 2007 Elsevier B.V. All rights reserved.