Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31389
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Cui, Tianxiang
Li, Jingpeng
Woodward, John R
Parkes, Andrew J
Contact Email: jli@cs.stir.ac.uk
Title: An ensemble based Genetic Programming system to predict English football premier league games
Citation: Cui T, Li J, Woodward JR & Parkes AJ (2013) An ensemble based Genetic Programming system to predict English football premier league games. In: 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), Singapore, Singapore, 16.04.2013-19.04.2013. Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/eais.2013.6604116
Issue Date: Apr-2013
Conference Name: 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)
Conference Dates: 2013-04-16 - 2013-04-19
Conference Location: Singapore, Singapore
Abstract: Predicting the result of a football game is challenging due to the complexity and uncertainties of many possible influencing factors involved. Genetic Programming (GP) has been shown to be very successful at evolving novel and unexpected ways of solving problems. In this work, we apply GP to the problem of predicting the outcomes of English Premier League games with the result being either win, lose or draw. We select 25 features from each game as the inputs to our GP system, which will then generate a function to predict the result. The experimental test on the prediction accuracy of a single GP-generated function is promising. One advantage of our GP system is, by implementing different runs or using different settings, it can generate as many high quality functions as we want. It has been showed that combining the decisions of a number of classifiers can provide better results than a single one. In this work, we combine 43 different GP-generated functions together and achieve significantly improved system performance.
Status: VoR - Version of Record
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