Immune centroids oversampling method for binary classification.

Abstract

To improve the classification performance of imbalanced learning, a novel oversampling method, immune centroids oversampling technique (ICOTE) based on an immune network, is proposed. ICOTE generates a set of immune centroids to broaden the decision regions of the minority class space. The representative immune centroids are regarded as synthetic examples in order to resolve the imbalance problem. We utilize an artificial immune network to generate synthetic examples on clusters with high data densities, which can address the problem of synthetic minority oversampling technique (SMOTE), which lacks reflection on groups of training examples. Meanwhile, we further improve the performance of ICOTE via integrating ENN with ICOTE, that is, ICOTE + ENN. ENN disposes the majority class examples that invade the minority class space, so ICOTE + ENN favors the separation of both classes. Our comprehensive experimental results show that two proposed oversampling methods can achieve better performance than the renowned resampling methods.