Dual autoencoders features for imbalance classification problem

Abstract

Many classification problems encountered in real-world applications exhibit a profile of imbalanced data. Current methods depend on data resampling. In fact, if the feature set provides a clear decision boundary, resampling may not be needed to solve the imbalanced classification problem. Therefore, this work proposes a feature learning method based on the autoencoder to learn a set of features with better classification capabilities of the minority and the majority classes to address the imbalanced classification problems. Two sets of features are learned by two stacked autoencoders with different activation functions to capture different characteristics of the data and they are combined to form the Dual Autoencoding Features. Samples are then classified in the new feature space learned in this manner instead of the original input space. Experimental results show that the proposed method outperforms current resampling-based methods with statistical significance for imbalanced pattern classification problems.