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2015 | OriginalPaper | Buchkapitel

A Probabilistic Rating System for Team Competitions with Individual Contributions

verfasst von : Sergey Nikolenko

Erschienen in: Analysis of Images, Social Networks and Texts

Verlag: Springer International Publishing

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Abstract

We study the problem of constructing a probabilistic rating system for team competitions. Unlike previous studies, we consider a setting where the competition can be broken down into relatively small individual tasks, and it is reasonable to assume that each task is done by a single team member. We begin with a simplistic naïve Bayes approach which is this case reduces to logistic regression and then develop it into a more complex model with latent variables trained by expectation–maximization. We show experimental results that validate our approach.

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Literatur
1.
Zurück zum Zitat Elo, A.: The Ratings of Chess Players: Past and Present. Arco, New York (1978) Elo, A.: The Ratings of Chess Players: Past and Present. Arco, New York (1978)
2.
Zurück zum Zitat Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5, 975–1005 (2004)MathSciNetMATH Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5, 975–1005 (2004)MathSciNetMATH
3.
Zurück zum Zitat Huang, T.K., Weng, R.C., Lin, C.J.: Generalized bradley-terry models and multi-class probability estimates. J. Mach. Learn. Res. 7, 85–115 (2006)MathSciNetMATH Huang, T.K., Weng, R.C., Lin, C.J.: Generalized bradley-terry models and multi-class probability estimates. J. Mach. Learn. Res. 7, 85–115 (2006)MathSciNetMATH
4.
Zurück zum Zitat Stein, A., Aryal, J., Gort, G.: Generalized bradley-terry models and multi-class probability estimates. IEEE Trans. Geosci. Remote Sens. 43, 852–856 (2005)CrossRef Stein, A., Aryal, J., Gort, G.: Generalized bradley-terry models and multi-class probability estimates. IEEE Trans. Geosci. Remote Sens. 43, 852–856 (2005)CrossRef
5.
Zurück zum Zitat Coulom, R.: Computing Elo ratings of move patterns in the game of Go. ICGA J. 30(4), 198–208 (2007) Coulom, R.: Computing Elo ratings of move patterns in the game of Go. ICGA J. 30(4), 198–208 (2007)
6.
Zurück zum Zitat Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: Proceedings of the \(27^{\text{ th }}\) International Conference on Machine Learning, pp. 13–20 (2010) Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: Proceedings of the \(27^{\text{ th }}\) International Conference on Machine Learning, pp. 13–20 (2010)
7.
Zurück zum Zitat Graepel, T., Minka, T., Herbrich, R.: TrueSkill(tm): a bayesian skill rating system. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, pp. 569–576. MIT Press, Cambridge (2007) Graepel, T., Minka, T., Herbrich, R.: TrueSkill(tm): a bayesian skill rating system. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, pp. 569–576. MIT Press, Cambridge (2007)
8.
Zurück zum Zitat Bradley, R.A., Terry, M.E.: Rank analysis of incomplete block designs. I. The method of paired comparisons. Biometrika 39, 324–245 (1952)MathSciNetMATH Bradley, R.A., Terry, M.E.: Rank analysis of incomplete block designs. I. The method of paired comparisons. Biometrika 39, 324–245 (1952)MathSciNetMATH
9.
Zurück zum Zitat Agresti, A.: Categorical Data Analysis. Wiley, New York (1990)MATH Agresti, A.: Categorical Data Analysis. Wiley, New York (1990)MATH
12.
Zurück zum Zitat Marden, J.I.: Analyzing and Modeling Rank Data. Chapman and Hall, London (1995)MATH Marden, J.I.: Analyzing and Modeling Rank Data. Chapman and Hall, London (1995)MATH
13.
Zurück zum Zitat Menke, J.E., Martinez, T.R.: A bradley-terry artificial neural network model for individual ratings in group competitions. Neural Comput. Appl. 17(2), 175–186 (2008)CrossRef Menke, J.E., Martinez, T.R.: A bradley-terry artificial neural network model for individual ratings in group competitions. Neural Comput. Appl. 17(2), 175–186 (2008)CrossRef
14.
Zurück zum Zitat Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)MATH Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)MATH
15.
Zurück zum Zitat Nikolenko, S.I., Sirotkin, A.V.: A new bayesian rating system for team competitions. In: Proceedings of the \(28^{\text{ th }}\) International Conference on Machine Learning, pp. 601–608 (2011) Nikolenko, S.I., Sirotkin, A.V.: A new bayesian rating system for team competitions. In: Proceedings of the \(28^{\text{ th }}\) International Conference on Machine Learning, pp. 601–608 (2011)
16.
Zurück zum Zitat Nikolenko, S.I., Serdyuk, D.V., Sirotkin, A.V.: Bayesian rating systems with additional information on tournament results. SPIIRAS Proc. 22, 189–204 (2012) Nikolenko, S.I., Serdyuk, D.V., Sirotkin, A.V.: Bayesian rating systems with additional information on tournament results. SPIIRAS Proc. 22, 189–204 (2012)
17.
Zurück zum Zitat Zhang, H.: The optimality of naive bayes. In: Barr, V., Markov, Z. (eds.) Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004). AAAI Press (2004) Zhang, H.: The optimality of naive bayes. In: Barr, V., Markov, Z. (eds.) Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004). AAAI Press (2004)
18.
Zurück zum Zitat Zhang, H., Su, J.: Naive bayesian classifiers for ranking. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 501–512. Springer, Heidelberg (2004) CrossRef Zhang, H., Su, J.: Naive bayesian classifiers for ranking. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 501–512. Springer, Heidelberg (2004) CrossRef
19.
Zurück zum Zitat Ward, G., Hastie, T., Barry, S., Elith, J., Leathwick, J.R.: Presence-only data and the EM algorithm. Biometrics 65(2), 554–563 (2009)MathSciNetCrossRefMATH Ward, G., Hastie, T., Barry, S., Elith, J., Leathwick, J.R.: Presence-only data and the EM algorithm. Biometrics 65(2), 554–563 (2009)MathSciNetCrossRefMATH
20.
Zurück zum Zitat Royle, J.A., Chandler, R.B., Yackulic, C., Nichols, J.D.: Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods Ecol. Evol. 3(3), 545–554 (2012)CrossRef Royle, J.A., Chandler, R.B., Yackulic, C., Nichols, J.D.: Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods Ecol. Evol. 3(3), 545–554 (2012)CrossRef
21.
Zurück zum Zitat Divino, F., Golini, N., Lasinio, G.J., Penttinen, A.: Bayesian modeling and MCMC computation in linear logistic regression for presence-only data (2013). arXiv:1305.1232 [stat.CO] Divino, F., Golini, N., Lasinio, G.J., Penttinen, A.: Bayesian modeling and MCMC computation in linear logistic regression for presence-only data (2013). arXiv:​1305.​1232 [stat.CO]
22.
Zurück zum Zitat Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 213–220. ACM, New York (2008) Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 213–220. ACM, New York (2008)
24.
Zurück zum Zitat Ling, C.X., Huang, J., Zhang, H.: AUC: a statistically consistent and more discriminating measure than accuracy. Proc. Int. Joint Conf. Artif. Intel. 2003, 519–526 (2003) Ling, C.X., Huang, J., Zhang, H.: AUC: a statistically consistent and more discriminating measure than accuracy. Proc. Int. Joint Conf. Artif. Intel. 2003, 519–526 (2003)
Metadaten
Titel
A Probabilistic Rating System for Team Competitions with Individual Contributions
verfasst von
Sergey Nikolenko
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-26123-2_1