An Improved Weighted ELM with Hierarchical Feature Representation for Imbalanced Biomedical Datasets

In medical intelligent diagnosis, most of the real-world datasets have the class-imbalance problem and some strong correlation features. In this paper, a novel classification model with hierarchical feature representation is proposed to tackle small and imbalanced biomedicine datasets. The main idea of the proposed method is to integrate extreme learning machine-autoencoder (ELM-AE) into the weighted ELM (W-ELM) model. ELM-AE with norm optimization is utilized to extract more effective information from raw data, thereby forming a hierarchical and compact feature representation. Afterwards, random projections of learned feature results view as inputs of the W-ELM. An adaptive weighting scheme is designed to reduce the misclassified rate of the minority class by assigning a larger weight to minority samples. The classification performance of the proposed method is evaluated on two biomedical datasets from the UCI repository. The experimental results show that the proposed method cannot only effectively solve the class-imbalanced problem with small biomedical datasets, but also obtain a higher and more stable performance than other state-of-the-art classification methods.