Abstract: In order to obtain a better feature subset in the text and eliminate interference and redundant features, a hybrid feature optimization algorithm  combining filtering and swarm intelligence algorithm is proposed. Firstly, the information gain value of each feature word is calculated, the better feature is selected as the preselected feature set, and then the sine cosine algorithm is used to optimize the preselected feature to obtain the selected feature set. In order to better balance the global search and local development capabilities in the sine-cosine algorithm, adaptive inertia weights are added. To more accurately evaluate feature subsets, a fitness function weighted by the number of features and accuracy is introduced, and a new location update mechanism is proposed. Experiment results on KNN and Bayesian classifier show that this feature selection model improves the classification accuracy, compared with other feature selection methods and the model before improvement.

Key words: feature selection, sine and cosine, inertia weight, classification accuracy