- Title
- Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football
- Creator
- Jelinek, Herbert; Kelarev, Andrei; Robinson, Dean; Stranieri, Andrew; Cornforth, David
- Date
- 2014
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/35699
- Identifier
- vital:5315
- Identifier
-
https://doi.org/10.1016/j.asoc.2013.08.010
- Identifier
- ISSN:1568-4946
- Abstract
- 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). © 2013 Published by Elsevier B.V. All rights reserved.; C1
- Relation
- Applied Soft Computing Vol. 14, no. PART A (2014), p. 81-87
- Rights
- Copyright Elsevier
- Rights
- This metadata is freely available under a CCO license
- Subject
- Australian football; Data mining; Feature selection; Heart rate dynamics; Meta regression; Regression; 0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 0806 Information Systems
- Reviewed
- Hits: 3844
- Visitors: 3543
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|