Upper body power (UBP) is one of the most important factors affecting the performance of cross-country skiers during races. Although some initial studies have already attempted to predict UBP, until now, no study has attempted to apply machine learning methods combined with various feature selection algorithms to identify the discriminative features for prediction of UBP. 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. Also, using less number of predictor variables than the full set of predictor variables can be useful for prediction of UBP10 and UBP60 with comparable error rates.
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About the Author
Mustafa Mikail Ozciloglu was born in Kilis-Turkey, in 1985. He completed the primary education and went to Haci Mehmet Kocarslan Anatolian high school in Kilis. He graduated from the department of Computer Engineering, Mersin University, in 2006. He received MSc degree in the department of Computer Engineering from TOBB Economy and Technology University. He has been working as a computer engineer in IT department of Kilis
University since 2010.