A new feature selection algorithm using forest optimization algorithm was proposed, which aimed at solving the problems of the traditional feature selection using forest optimization algorithm in the stages of initialization, candidate forest generation and updating. In the algorithm, Pearson correlation coefficient and L1 regularization method were used to replace the random initialization strategy in the initialization stage, the methods of separating good and bad trees and fulfilling the difference were used to solve the problems of incompletion of good and bad trees in the candidate forest generation stage, and trees having the same precision but different dimension with the optimal tree were added to the forest in the updating stage. In the experiments, with the same experimental data and experimental parameters, the proposed algorithm and the traditional feature selection using forest optimization algorithm were used to test the small, medium and large dimension data respectively. The experimental results show that the proposed algorithm is better than the traditional feature selection using forest optimization algorithm in the classification performance and dimension reduction ability on two medium and two large dimension data. The experimental results prove the effectiveness of the proposed algorithm in solving feature selection problems.
