An Ensemble Based Genetic Programming System to Predict English Football Premier League Games

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{football,
  author =       "Tianxiang Cui and Jingpeng Li and John R. Woodward and 
                 Andrew J. Parkes",
  title =        "An Ensemble Based Genetic Programming System to
                 Predict English Football Premier League Games",
  booktitle =    "2013 IEEE Symposium Series on Computational
                 Intelligence",
  year =         "2013",
  editor =       "P. N. Suganthan",
  pages =        "138--143",
  address =      "Singapore",
  month =        "16-19 " # apr,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/EAIS.2013.6604116",
  size =         "6 pages",
  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.",
  notes =        "http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/

                 EAIS 2013, also known as \cite{6604116}",
}

Genetic Programming entries for Tianxiang Cui Jingpeng Li John R Woodward Andrew J Parkes

Citations