Feature selection based on improved principal component analysis

The filtered feature selection method has low computational complexity and less time, and is widely used in feature selection, but the filtered method only considers the importance of features for label classification and ignores the correlation between features. For this reason, a feature selection method with improved principal component analysis is proposed. The main idea of the method is that on the basis of principal components, the loadings of each indicator on different principal components and their variance contribution ratios with that principal component are considered. A number of indicators with the largest cumulative contribution rates were selected, so that the final extracted indicators retained more information. Subsequently, comparative experiments are conducted using the UCI dataset, and the results show that the approach proposed in this paper has some superiority over other methods. Finally, the features of China's green innovation efficiency are selected using the approach proposed in this paper to demonstrate the feasibility of the method.