SMOTE-Stacked Sparse Autoencoder Over-Sampling Algorithm and Fuzzy ARTMAP for Imbalanced Data Classification

Synthetic Minority Over-sampling Technique (SMOTE) is a popular over-sampling method to tackle imbalanced class problem. However, SMOTE could produce noisy samples during over-sampling. To overcome the shortcoming of SMOTE, this paper presents a combination of SMOTE, Stacked Sparse Autoencoder (SSAE), and Fuzzy ARTMAP (FAM), namely SMOTE-SSAE-FAM. In the proposed method, SMOTE is applied to over-sample the minority-class samples. Next, all the generated synthetic samples are transformed by SSAE in order to deal with the noises. FAM that performs incremental learning is then applied to learn information from the balanced data sets. The performance of SMOTE-SSAE-FAM is evaluated using 40 benchmark data sets from public portals. The experimental results show that the proposed SMOTE-SSAE-FAM has achieved an excellent performance as compared with other state-of-art SMOTE methods in classifying imbalanced data.