Perceptron learning for classification problems: Impact of cost-sensitivity and outliers robustness

In learning approaches for classification problem, the misclassification error types may have different impacts. To take into account this notion of misclassification cost, cost sensitive learning algorithms have been proposed, in particular for the learning of multilayer perceptron. Moreover, data are often corrupted with outliers and in particular with label noise. To respond to this problem, robust criteria have been proposed to reduce the impact of these outliers on the accuracy of the classifier. This paper proposes to associate a cost sensitivity weight to a robust learning rule in order to take into account simultaneously these two problems. The proposed learning rule is tested and compared on a simulation example. The impact of the presence or absence of outliers is investigated. The influence of the costs is also studied. The results show that the using of conjoint cost sensitivity weight and robust criterion allows to improve the classifier accuracy.