Creating synthetic minority class samples based on autoencoder extreme learning machine

Abstract

This paper reports a new method (simplified as AE-ELM-SynMin) to create the Synthetic Minority class samples for imbalanced classification based on AutoEncoder Extreme Learning Machine (AE-ELM). AE-ELM-SynMin first trains an AE-ELM which is a special ELM with the same input and output, i.e., the original minority class samples. Second, the crossover, mutation and filtration operations are conducted on the hidden-layer output of AE-ELM and then the synthetic hidden-layer output is obtained. Third, the synthetic minority class samples are created by decoding the synthetic hidden-layer output with output-layer weights of AE-ELM. AE-ELM-SynMin guarantees that the synthetic minority class has the higher information amount than original minority class and meanwhile keeps the consistent probability distribution with the original minority class. The experimental results demonstrate the better imbalanced classification performances of AE-ELM-SynMin in comparison with the regular synthetic minority over-sampling technique (Regular-SMOTE) and its variants, e.g., Borderline-SMOTE, Random-SMOTE, and SMOTE-IPF.