Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine

Abstract

The paper constructed a depression classification model based on emotionally related eye-movement data and kernel extreme learn machine (ELM). In order to improve the classification ability of the model, we use particle swarm optimization (PSO) to optimize the model parameters (regularization coefficient C and the parameter σ in the kernel function). At the same time, in order to avoid to be caught in the local optimum and improve PSO's searching ability, we use improved chaotic PSO optimization algorithm and Gauss mutation strategy to increase PSO's particle diversity. The classification results show that the accuracy, sensitivity and specificity of classification models without parameter optimization and Gauss mutation strategy are 80.23%, 80.31% and 79.43%, respectively, while those results of classification model using improved chaotic projection model and Gauss mutation strategy are improved to 88.55%, 87.71% and 89.42%, respectively. Compared with other classification methods of depression, the proposed classification method has better performance on depression recognition.