A Novel Ensemble Approach Based on Balanced Perceptrons Applied to Microarray Datasets

Recently, ensemble learning theory has received much attention in the machine learning community, since it has been demonstrated as a great alternative to generate more accurate predictors with higher generalization abilities. The improvement of generalization performance of an ensemble is directly related to the diversity and accuracy of the individual classifiers. Thus, contributions in this scenario are still relevant. In this paper, we propose a novel ensemble approach based on balanced Perceptrons. In order to improve the accuracy of each individual classifier, we balance the final hyper plane solution. Also, we introduce the dissimilarity measure which is employed in order to maximize the diversity of the ensemble. This strategy accepts a new component in the ensemble only if it holds a minimum predetermined distance from the other components. We conduct our experimental study on micro array datasets and assess the performance of the proposed method combined by averaging and unweighted voting. Reported results show that our method outperforms other ensemble approaches, such as Random Averaging and AdaBoost, in all considered datasets. Also, we overcome Support Vector Machines in almost all cases. We perform statistical tests to check for the significance of our results.