A Feature Selection Method Based on Adaptive Differential Evolution

This paper proposes a feature selection method based on adaptive differential evolution-ISHADEFS. By using mutual information and Pearson correlation coefficient to construct a new fitness function, the enhancement effect of correlation and similarity is used to improve the efficiency and accuracy of filtering. A triangular mutation operator is improved, and the operator diversity is enhanced by the operator adaptive structure to improve the performance of the algorithm. The experimental results show that compared to conventional filtering methods and original evolution methods, ISHADEFS can achieve better classification performance.