- Title
- Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football
- Creator
- Jelinek, Herbert F.; Kelarev, Andrei; Robinson, Dean J.; Stranieri, Andrew; Cornforth, David J.
- Relation
- Applied Soft Computing Journal Vol. 14, Issue PART A, p. 81-87
- Publisher Link
- http://dx.doi.org/10.1016/j.asoc.2013.08.010
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2014
- Description
- This work investigates the effectiveness of using computer-based machine learning regression algorithms and meta-regression methods to predict performance data for Australian football players based on parameters collected during daily physiological tests. Three experiments are described. The first uses all available data with a variety of regression techniques. The second uses a subset of features selected from the available data using the Random Forest method. The third used meta-regression with the selected feature subset. Our experiments demonstrate that feature selection and meta-regression methods improve the accuracy of predictions for match performance of Australian football players based on daily data of medical tests, compared to regression methods alone. Meta-regression methods and feature selection were able to obtain performance prediction outcomes with significant correlation coefficients. The best results were obtained by the additive regression based on isotonic regression for a set of most influential features selected by Random Forest. This model was able to predict athlete performance data with a correlation coefficient of 0.86 (p < 0.05).
- Subject
- feature selection; regression; meta regression; data mining; heart rate dynamics; Australian football
- Identifier
- http://hdl.handle.net/1959.13/1066012
- Identifier
- uon:18002
- Identifier
- ISSN:1568-4946
- Language
- eng
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