Abstract: With the development of educational informatization, educational data presents characteristics such as high feature counts and high redundancy, resulting in the classification accuracy of current classification algorithms not being ideal on educational data. Therefore, this paper proposes a hybrid feature selection algorithm (RF-ATPSO) that integrates feature weighting algorithm with improved particle swarm optimization algorithm. The algorithm first uses the RELIEF-F algorithm to calculate the weights of each feature, removes redundant features, and then uses the improved particle swarm optimization algorithm to search for the optimal feature subset in the filtered feature set. Experimental results show that on 6 UCI public datasets, after feature selection using the RF-ATPSO algorithm, the average accuracy is improved by 10.04%, and the average feature subset size is the smallest and the convergence speed is the fastest. In the student academic performance portrait feature dataset, the algorithm achieves high classification accuracy with a smaller feature subset size, with an average accuracy of 94.77%, which is significantly better than other feature selection algorithms. The experiment fully demonstrates the practical application significance of this algorithm.

Key words: feature selection, feature weight, improved PSO, T-distribution