Instance Selection in the Performance of Gamma Associative Classifier

Abstract

The Gamma associative classifier is among the most used classifiers of the alpha-beta associative approach. It had been used successfully to solve many Pattern Recognition tasks, including environmental applications. However, as most classifiers, Gamma suffers with the presence of noisy or mislabeled instances in the training sets. This paper evaluates the impact of using instance selection techniques in the performance of Gamma classifier. The numerical experiments carried out over well-known repository datasets allows to conclude that instance selection may increase the testing accuracy of the Gamma classifier.