Dual autoencoders features for imbalance classification problem

Highlights：

• To our best knowledge, this paper proposes the first feature learning-based method for dealing with imbalance pattern classification using stacked autoencoders. We provide a new viewpoint for solving imbalanced pattern classification problems by projecting the input space onto a learned feature space with better representation using stacked autoencoders.

• The concatenation of learned features from two different stacked autoencoders further enhances the classification accuracy by providing a better representation with different characteristics of the data to the classifier to learn. More specifically, the sigmoid activation function is less sensitive to input changes and provides robust representation while the tanh activation function is more sensitive and provides detailed information of the data.

• Experimental results show that the DAF yields statistical significant improvement in the AUC, the F1-score and the G-Mean in comparison to representative methods of resampling-based and feature projection methods for dealing with the imbalanced pattern classification problems.