• DocumentCode
    3149493
  • Title

    Using neural networks to predict binary outcomes

  • Author

    Ong, Enn ; Flitman, Andrew

  • Author_Institution
    Dept. of Bus. Syst., Monash Univ., Clayton, Vic., Australia
  • Volume
    1
  • fYear
    1997
  • fDate
    28-31 Oct 1997
  • Firstpage
    427
  • Abstract
    Predicting binary outcomes with respect to sports typically involves the use of human judgement and subjectivity. This research considers the use of neural networks and logistic regression as an interesting and quantifiable alternative to human tipsters, whose predictions are of a qualitative nature. Using a set of naive but objective input variables, we highlight the improvement in prediction accuracy obtained through the use of neural networks and logistic regression. A comparison between neural networks, logistic regression and human tipping experts is presented
  • Keywords
    forecasting theory; neural nets; sport; statistical analysis; binary outcome prediction; human judgement; human subjectivity; human tipping experts; logistic regression; naive objective input variables; neural networks; prediction accuracy; qualitative predictions; sport; tipsters; Accuracy; Australia; CD-ROMs; Humans; Information technology; Input variables; Logistics; Neural networks; Predictive models; Statistical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7803-4253-4
  • Type

    conf

  • DOI
    10.1109/ICIPS.1997.672816
  • Filename
    672816