TY - GEN
T1 - An ensemble based Genetic Programming system to predict English football premier league games
AU - Cui, Tianxiang
AU - Li, Jingpeng
AU - Woodward, John R.
AU - Parkes, Andrew J.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84885202688&partnerID=8YFLogxK
U2 - 10.1109/EAIS.2013.6604116
DO - 10.1109/EAIS.2013.6604116
M3 - Conference contribution
AN - SCOPUS:84885202688
SN - 9781467358552
T3 - Proceedings of the 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
SP - 138
EP - 143
BT - Proceedings of the 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
PB - IEEE Computer Society
T2 - 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -