Feature Selection Algorithm Based on Sparse Score and Correlation Analysis

Aiming at the high computational complexity of classification prediction algorithms for high-dimensional data with large scale and high dimensionality, an effective solution is to select a small number of feature subsets with high correlations among the many candidate features of high-dimensional data, and remove the irrelevant and redundant features. In this paper, based on the correlation of sparse scores and category features, the feature selection (ISSFS) algorithm based on sparse score and correlation analysis is studied to select the input features of the learning algorithm. The algorithm calculates the optimal feature subset by comprehensively analyzing the sparse score of each feature in the dataset and the degree of correlation between the feature and the category, so as to achieve the purpose of dimension reduction of high-dimensional data features. Simulation experiments show that the algorithm achieves better feature selection on UCI dataset and ice hockey game dataset, and the classification effect is good.