Feature extraction technique based on Shapley value method and improved mRMR algorithm

Feature extraction techniques are widely used in fields such as machine learning, pattern recognition, and image processing. The quality of feature extraction is crucial to the generalization ability of a model. This paper proposes a feature extraction technique based on the Shapley value method and an improved Minimum Redundancy Maximum Relevance (mRMR) analysis method. The improved mRMR algorithm enhances the recognition ability for fitting effects of multi-variable feature subsets by traversing all possible combinations of input variable subsets. That is, the result of feature subset selection with fewer input variables does not affect the feature subset selection process when there are more input variables, thus avoiding the limitations of the original algorithm. The research results indicate that using the Shapley value method in conjunction with the improved mRMR algorithm proposed in this study can select the optimal feature subset containing fewer feature variables and achieve a lower MSE value. This contributes to achieving lower computational complexity and higher data fitting accuracy. This technique is applied to the feature extraction scenarios of power battery output power signals and instantaneous fuel consumption signals in hybrid electric vehicles, and constructs the optimal feature subsets for the aforementioned signals. •Exhibits superior global search capabilities, achieving improved data fitting results.•Improves the construction efficiency of the optimal feature subset.•Helps to improve the data fitting accuracy of the plant model.