Hui Wen

Ensemble learning-based online sequential pre-interference extreme learning for concept drifting and class imbalanced data streams

The proposed model introduces a pre-interference layer between the input layer and hidden layer of the original OS-ELM to enhance nonlinear feature representation through kernel-like transformation of sequential data, thereby improving the discriminative ability of different classes.