Collaborative Feature Selection Optimization Based on Improved Wolf Pack Algorithm

In order to improve the problem of low precision in solving the data classification problem of the traditional support vector machine, this paper combines SVM classification with feature selection synchronously, and the intelligent optimization algorithm of the wolf pack is first improved, and the improvement methods such as Intuitive local search, Cauchy mutation strategy, Gaussian mutation strategy are added, and then the improved wolf pack algorithm is used to optimize the kernel function and feature selection at the same time to obtain the selected feature classification results. By comparing a variety of different algorithms in sixteen sets of classical UCI data sets, there are significant advantages in the evaluation of different indicators, and the experimental results show that the proposed algorithms can process the data more accurately and avoid the interference of redundant features.