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Algorithms for Prediction of Upper Body Power of Cross-Country Skiers
Prediction of Upper Body Power of Cross-Country Skiers Using Machine Learning Methods Combined With Feature Selection
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Upper body power (UBP) is one of the most important factors affecting the performance of cross-country skiers during races. The purpose of this study is to develop new prediction models for predicting the 10-second UBP (UBP10) and 60-second UBP (UBP60) of cross-country skiers by using General Regression Neural Networks (GRNN), Radial-Basis Function Network (RBF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Single Decision Tree (SDT) and Tree Boost (TB) along with the Relief-F feature selection algorithm, minimum redundancy maximum relevance (mRMR) feature selection algorithm and the Correlation-based Feature Subset Selection (CFS). Several models have been developed to predict UBP10 and UBP60 of cross-country skiers using two datasets. 10-fold cross validation has been performed for model testing. The efficiency of the prediction models has been calculated with their multiple correlation coefficients (R's), standard error of estimates (SEE's) and mean absolute percentage errors (MAPE's). The results emphasize that GRNN-based prediction models show higher performance than the other regression methods.
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