Machine learning of swimming data via wisdom of crowd and regression analysis

Jiang Xie; Junfu Xu; Celine Nie; Qing Nie; Jiang Xie; Junfu Xu; Celine Nie; Qing Nie

doi:10.3934/mbe.2017031

Mathematical Biosciences and Engineering

2017, Volume 14, Issue 2: 511-527. doi: 10.3934/mbe.2017031

Previous Article Next Article

Machine learning of swimming data via wisdom of crowd and regression analysis

1.
School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai 200444, China
2.
University High School, 4771 Campus Drive, Irvine, CA 92612, USA
3.
Department of Mathematics, Center for Mathematical and Computational Biology, University of California, Irvine, CA 92697, USA

Received: 01 July 2016 Accepted: 05 August 2016 Published: 01 April 2017
MSC : 97R40, 92R30, 00A69

Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.
- Big data,
- statistical analysis,
- prediction,
- swimming model,
- time series data
Citation: Jiang Xie, Junfu Xu, Celine Nie, Qing Nie. Machine learning of swimming data via wisdom of crowd and regression analysis[J]. Mathematical Biosciences and Engineering, 2017, 14(2): 511-527. doi: 10.3934/mbe.2017031

Related Papers:

Abstract

Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.

References

[1]	[ M. Bächlin,G. Tröster, Swimming performance and technique evaluation with wearable acceleration sensors, Pervasive and Mobile Computing, 8 (2012): 68-81.
[2]	[ R. C. Barros,M. P. Basgalupp,A. C. De Carvalho,A. Freitas, A survey of evolutionary algorithms for decision-tree induction, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42 (2012): 291-312.
[3]	[ D. Basak,S. Pal,D. C. Patranabis, Support vector regression, Neural Information Processing-Letters and Reviews, 11 (2007): 203-224.
[4]	[ C. Cai,G. Wang,Y. Wen,J. Pei,X. Zhu,W. Zhuang, Superconducting transition temperature t c estimation for superconductors of the doped mgb2 system using topological index via support vector regression, Journal of Superconductivity and Novel Magnetism, 23 (2010): 745-748.
[5]	[ D. Cai, X. He and J. Han, Semi-supervised discriminant analysis, in Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, IEEE, 2007, 1-7.
[6]	[ J. Cao,S. Kwong,R. Wang, A noise-detection based adaboost algorithm for mislabeled data, Pattern Recognition, 45 (2012): 4451-4465.
[7]	[ J. J. Cheh,R. S. Weinberg,K. C. Yook, An application of an artificial neural network investment system to predict takeover targets, Journal of Applied Business Research (JABR), 15 (2013): 33-46.
[8]	[ J. L. Dye,V. A. Nicely, A general purpose curve fitting program for class and research use, Journal of chemical Education, 48 (1971): 443.
[9]	[ M. A. Friedl,C. E. Brodley, Decision tree classification of land cover from remotely sensed data, Remote Sensing of Environment, 61 (1997): 399-409.
[10]	[ K. Fukunaga,P. M. Narendra, A branch and bound algorithm for computing k-nearest neighbors, Computers, IEEE Transactions on, 100 (1975): 750-753.
[11]	[ A. Garg and K. Tai, Comparison of regression analysis, artificial neural network and genetic programming in handling the multicollinearity problem, in Modelling, Identification & Control (ICMIC), 2012 Proceedings of International Conference on, IEEE, 2012,353-358.
[12]	[ Z. Guo,W. Zhao,H. Lu,J. Wang, Multi-step forecasting for wind speed using a modified emd-based artificial neural network model, Renewable Energy, 37 (2012): 241-249.
[13]	[ I. Hmeidi,B. Hawashin,E. El-Qawasmeh, Performance of knn and svm classifiers on full word arabic articles, Advanced Engineering Informatics, 22 (2008): 106-111.
[14]	[ Y. Jiang, J. Lin, B. Cukic and T. Menzies, Variance analysis in software fault prediction models, in Software Reliability Engineering, 2009. ISSRE'09. 20th International Symposium on, IEEE, 2009, 99-108.
[15]	[ A. Liaw,M. Wiener, Classification and regression by randomforest, R news, 2 (2002): 18-22.
[16]	[ B. Liu and G. Qiu, Illuminant classification based on random forest, in Machine Vision Applications (MVA), 2015 14th IAPR International Conference on, IEEE, 2015,106-109.
[17]	[ D. Marbach,R. J. Prill,T. Schaffter,C. Mattiussi,D. Floreano,G. Stolovitzky, Revealing strengths and weaknesses of methods for gene network inference, Proceedings of the National Academy of Sciences, 107 (2010): 6286-6291.
[18]	[ F. Pedregosa,G. Varoquaux,A. Gramfort,V. Michel,B. Thirion,O. Grisel,M. Blondel,P. Prettenhofer,R. Weiss,V. Dubourg,J. Vanderplas,A. Passos,D. Cournapeau,M. Brucher,M. Perrot,E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, 12 (2011): 2825-2830.
[19]	[ M.-T. Puth,M. Neuhäuser,G. D. Ruxton, Effective use of pearson's product--moment correlation coefficient, Animal Behaviour, 93 (2014): 183-189.
[20]	[ G. Rätsch,T. Onoda,K.-R. Müller, Soft margins for adaboost, Machine Learning, 42 (2001): 287-320.
[21]	[ J. F. Reis,F. B. Alves,P. M. Bruno,V. Vleck,G. P. Millet, Oxygen uptake kinetics and middle distance swimming performance, Journal of Science and Medicine in Sport, 15 (2012): 58-63.
[22]	[ B. Scholkopft,K.-R. Mullert, Fisher discriminant analysis with kernels, Neural Networks for Signal Processing Ⅸ, 1 (1999): p1.
[23]	[ C. Schüldt, I. Laptev and B. Caputo, Recognizing human actions: A local svm approach, in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, IEEE, 3 (2004), 32-36.
[24]	[ A. J. Smola,B. Schölkopf, A tutorial on support vector regression, Statistics and Computing, 14 (2004): 199-222.
[25]	[ M. Vaso,B. Knechtle,C. A. Rüst,T. Rosemann,R. Lepers, Age of peak swim speed and sex difference in performance in medley and freestyle swimming. a comparison between 200 m and 400 m in swiss elite swimmers, Journal of Human Sport and Exercise, 8 (2013): 954-965.
[26]	[ Q. Wang,G. M. Garrity,J. M. Tiedje,J. R. Cole, Naive bayesian classifier for rapid assignment of rrna sequences into the new bacterial taxonomy, Applied and Environmental Microbiology, 73 (2007): 5261-5267.
[27]	[ S.-C. Wang, Artificial neural network, in Interdisciplinary Computing in Java Programming, Springer, 2003, 81-100.
[28]	[ C.-H. Wu,J.-M. Ho,D.-T. Lee, Travel-time prediction with support vector regression, Intelligent Transportation Systems, IEEE Transactions on, 5 (2004): 276-281.
[29]	[ J. Wu, Z. Cai, S. Zeng and X. Zhu, Artificial immune system for attribute weighted naive bayes classification, in Neural Networks (IJCNN), The 2013 International Joint Conference on, IEEE, 2013, 1-8.

Reader Comments

Your name:*

Email:*
© 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)