Feature selection algorithm based on feature weights and improved particle swarm optimization
With the development of educational informatization,educational data presents character-istics 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 par-ticle 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 optimi-zation 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.
万方数据
维普
