Optimizing Weighted ELM Based on Gray Wolf Optimizer for Imbalanced Data Classification

Extreme Learning Machine (ELM), which is a Well-known supervised learning algorithm, provides a high generalization performance and a fast learning technique. However, ELM cannot handle the class imbalance. Recently, the weighted ELM was developed to solve this issue. The weighted ELM performance still faced with finding the proper boundary between positive class and negative class. In this paper, we proposed GWO-weighted ELM that is a combination of the weighted ELM integrated with the Gray wolf optimizer (GWO). The main contribution of the paper is the regularization parameter is optimized via the GWO algorithm. The experiments were conducted on 16 real imbalanced datasets. We compared the proposed GWO-weighted ELM with the original weighted ELM. The experimental results showed that the GWO-weighted ELM with Wl is superior to the weighted ELM and the GWO-weighted ELM with W2.