Performance Analysis for Exercise Pose Prediction Using RFE Based Feature Selection

This study uses Recursive Feature Elimination (RFE), to select the feature in Exercise Pose Prediction. Firstly, feature selection was performed based on RFE. Among them, eXtreme Gradient Boosting (XGBoost), Random Forest (RF) and Decision Tree (DT) were used as the base estimators for RFE, respectively. The number of selected features was adjusted from 1 to 98, and the step was maintained at 1, to identify the optimal feature combination in the Exercise Pose Prediction. Then, the XGBoost, RF, DT and LR classification algorithms were used to classify and predict the Exercise Pose Prediction features selected by RFE, respectively. Ay last, using the classification results of the classifiers', the performance metrics of the Exercise Pose Prediction were compared by classifiers type. The results show that XGBoost was more suitable for the data after RFE feature selection than RF and DT classifiers. When the base estimator of RFE was XGBoost, the number of selected features was 52, and the performance metrics of the XGBoost classifier were the highest.