Abstract: Feature selection is very important in the stage of data preprocessing.The quality of feature selection not only affects the training time of the neural network but also affects the performance of the neural network.Grey Wolf improved Flower pollination algorithm(Grey Wolf improved Flower pollination algorithm,GIFPA) is a hybrid algorithm based on the fusion of flower pollination algorithm framework and gray wolf optimization algorithm.When it is applied to feature selection,it can not only retain the connotation information of the original features but also maximize the accuracy of classification features.The GIFPA algorithm adds the worst individual information to the FPA algorithm,uses the cross-pollination stage of the FPA algorithm as the global search,uses the hunting process of the gray wolf optimization algorithm as the local search,and adjusts the search process of the two through the conversion coefficient.At the same time,to overcome the problem that swarms intelligence algorithm is easy to fall into local optimization,this paper uses the RelifF algorithm in the field of data mining to improve this problem and uses the RelifF algorithm to filter out high weight features and improve the best individual information.To verify the performance of the algorithm,21 classical data sets in the UCI database are selected for testing,k-nearest neighbor(KNN) classifier is used for classification and evaluation,fitness value and accuracy are used as evaluation criteria,and K-fold crossover verification is used to overcome the over-fitting problem.In the experiment,a variety of classical algorithms and advanced algorithms,including the FPA algorithm,are compared.The experimental results show that the GIFPA algorithm has strong competitiveness in feature selection.

Key words: Feature selection, FPA, GWO, Optimizer, RelifF