Weighted Two-Hidden-Layer Extreme Learning Machine Method with Improved Gray Wolf Optimization for Complex Data Classification

Abstract

Extreme Learning Machine (ELM) is widely popular for its advantages such as fast training speed and good generalization performance. However, the randomness of hidden layer parameters in ELM leads to unstable prediction performance of the model. We propose a novel two-hidden-layer extreme learning machine (TELM) for complex data classification. Firstly, the idea of weighting is introduced into TELM, and a weighted two-hidden-layer extreme learning machine (WTELM) model is proposed to improve the classification accuracy of the model. Secondly, the convergence factor and position update formula in gray wolf optimization algorithm (GWO) are adjusted to enhance the optimization algorithm’s ability to search for optimal parameters. Finally, an improved gray wolf optimization (IGWO) is utilized to search for the optimal parameters of the WTELM model. The impact of different intelligent optimization algorithms on the model’s classification results is compared. The experimental results demonstrate that the classification accuracy of the WTELM model has been improved by about 11–18% compared to traditional ELM models. Moreover, compared to the WTELM model, the classification accuracy of the IGWO-WTELM model has improved by about 1–19%. This indicates that the method proposed in this paper is significantly superior to traditional ELM methods and their variants, improving the stability, training speed and classification accuracy of the model.