A binary individual search strategy-based bi-objective evolutionary algorithm for high-dimensional feature selection

, which can search the global optimal feature combination. Thirdly, enhanced population entropy and improved average convergence rate are adopted to monitor the correlation between the diversity of the population and the convergence of optimization objectives. Promising experimental results on twelve high-dimensional datasets reveal that the proposed algorithm can obtain competitive classification accuracy and effectively reduce the size of feature subset compared with ten state-of-the-art evolutionary algorithms.