An integrated Extreme learning machine based on kernel risk-sensitive loss of q-Gaussian and voting mechanism for sample classification

Abstract

Ensemble learning is to train and combine multiple learners to complete the corresponding learning tasks. It can improve the stability of the overall model, and a good ensemble method can further improve the accuracy of the model. At the same time, as one of the outstanding representatives of machine learning, Extreme Learning Machine has attracted the continuous attention of experts and scholars. to get a better representation of the feature space, we extend the Gaussian kernel in the kernel risk-sensitive loss and propose a Kernel Risk-Sensitive Loss of q-Gaussian kernel and Hyper-graph Regularized Extreme Learning Machine method. Since the contingency in the ELM training process cannot be completely avoided, the stability of most ELM methods is affected to some extent. What’s more, we introduce the voting mechanism and a new ELM classification model named Kernel Risk-Sensitive Loss of q-Gaussian kernel and Hyper-graph Regularized Integrated Extreme Learning Machine based on Voting Mechanism is proposed. It improves the stability of the model through the idea of ensemble learning. We apply the new model on six real data sets, and through observation and analysis of experimental results, we find that the new model has certain competitiveness, especially in classification accuracy and stability.