Minority manifold regularization by stacked auto-encoder for imbalanced learning

Abstract

Imbalanced learning is considered one of the challenging problems in machine learning. This problem arises when a learning algorithm is biased toward the majority class due to the large proportion of the majority class data while detecting the minority class is of greater importance. In the present study, a novel method (MMRAE) is presented for imbalanced learning encompassing feature learning and classification steps. In the feature learning step, meaningful features are extracted from the minority data and their underlying manifold are captured by taking advantage of one-class learning approach through stacking two regularized auto-encoders. The existence of novel and different regularizers in each auto-encoder leads to a new representation with proper data discrimination which improves the between-class and within-class imbalanced problems. Then, in the classification step, the classification between the minority and majority class is performed by constructing a multilayer neural network using features learned throughout pre-training. The proposed method is extensively studied on six artificial and twenty real datasets in order to have a precise evaluation. Based on different criteria such as F-measure, G-mean, and AUC, the results represent considerable performance of the proposed method compared to several other existing methods.