Imbalanced data classification based on diverse sample generation and classifier fusion

Class imbalance problems are pervasive in many real-world applications, yet classifying imbalanced data remains to be a very challenging task in machine learning. SMOTE is the most influential oversampling approach. Based on SMOTE, many variants have been proposed. However, SMOTE and its variants have three drawbacks: (1) the probability distribution of the minority class samples is not considered; (2) the generated minority samples lack diversity; (3) the generated minority class samples overlap severely when oversampled many times for balancing with majority class samples. In order to overcome these three drawbacks, a generative adversarial network (GAN) based framework is proposed in this paper. The framework includes an oversampling method and a two-class imbalanced data classification approach. The oversampling method is based on an improved GAN model, and the classification approach is based on classifier fusion via fuzzy integral, which can well model the interactions among the base classifiers trained on the balanced data subsets constructed by the proposed oversampling method. Extensive experiments are conducted to compare the proposed methods with related methods on 5 aspects: MMD-score, Silhouette-score, F-measure, G-means, and AUC-area. The experimental results demonstrate that the proposed methods are more effective and efficient than the compared approaches.