A dual encoder DAE neural network for imbalanced binary classification based on NSGA-III and GAN

In real-world datasets, the number of samples in each class is often imbalanced, which results in the classifier's suboptimal performance. Presently, the imbalanced binary classification approach based on deep learning has achieved good results and gets more attention constantly. In this study, we present a dual encoder (Denoising Auto-Encoder) DAE neural network based on non-dominated sorting genetic algorithm (NSGA-III) and generative adversarial network (GAN) to address the imbalanced binary classification problem. The primary aim of our approach is to increase the separability between the reconstruction error of minority class latent features and the reconstruction error of majority class latent features. For this purpose, we first create a dual encoder DAE network to obtain the reconstruction error of latent features of training data. Second, when training the neural network, we introduced GAN to perform a layer-wise training which can improve the training effect of the model. Third, in order to increase the separability of the reconstruction error of minority class and majority class, we utilize NSGA-III to optimize the parameters of the second encoder. Then, we can obtain a set of non-dominated solutions. Finally, based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, we can get the best solution, which is the most appropriate parameter set of the second encoder to distinguish the minority class and the majority class. The experiment results on both benchmark datasets and a real-world dataset for communication anomaly detection demonstrate the superiority of the proposed approach in imbalanced binary classification problem.