SOCIAL: Self-Organizing ClassIfier ensemble for Adversarial Learning

Abstract

Pattern recognition techniques are often used in environments (called adversarial environments) where adversaries can consciously act to limit or prevent accurate recognition performance. This can be obtained, for example, by changing labels of training data in a malicious way. While Multiple Classifier Systems (MCS) are currently used in several security applications, like intrusion detection in computer networks and spam filtering, there are very few MCS proposals that explicitly address the problem of learning in adversarial environments. In this paper we propose a general algorithm based on a multiple classifier approach to find out and clean mislabeled training samples. We will report several experiments to verify the robustness of the proposed approach to the presence of possible mislabeled samples. In particular, we will show that the performance obtained with a simple classifier trained on the training set “cleaned” by our algorithm is comparable and even better than those obtained by some state-of-the-art MCS trained on the original datasets.