An Imbalanced Data Classification Algorithm of De-noising Auto-Encoder Neural Network Based on SMOTE

¹ Inner Mongolia University for the Nationalities, College of Mathematics, 028000 Tongliao, China
² Northeast Normal University, College of Computer Science and Information Technology, 130000 Changchun, China
³ Inner Mongolia University for the Nationalities, College of Computer Science and Technology, 028000 Tongliao, China

Abstract

Imbalanced data classification problem has always been one of the hot issues in the field of machine learning. Synthetic minority over-sampling technique (SMOTE) is a classical approach to balance datasets, but it may give rise to such problem as noise. Stacked De-noising Auto-Encoder neural network (SDAE), can effectively reduce data redundancy and noise through unsupervised layer-wise greedy learning. Aiming at the shortcomings of SMOTE algorithm when synthesizing new minority class samples, the paper proposed a Stacked De-noising Auto-Encoder neural network algorithm based on SMOTE, SMOTE-SDAE, which is aimed to deal with imbalanced data classification. The proposed algorithm is not only able to synthesize new minority class samples, but it also can de-noise and classify the sampled data. Experimental results show that compared with traditional algorithms, SMOTE-SDAE significantly improves the minority class classification accuracy of the imbalanced datasets.