Extracting optimal features from switch current curve using random forest

This paper proposes a feature selection method based on random forest to address the issues of high dimensionality and redundant features in switch data. Firstly, multidimensional features including average, maximum, minimum, gradient, etc. were extracted based on the S700K switch current curve; Subsequently, the random forest algorithm is used to evaluate the importance of each feature and select the optimal feature subset. This method provides high-quality feature inputs for the construction of subsequent fault diagnosis models, laying the foundation for optimizing model performance[1].