Title :
Using neural networks to predict binary outcomes
Author :
Ong, Enn ; Flitman, Andrew
Author_Institution :
Dept. of Bus. Syst., Monash Univ., Clayton, Vic., Australia
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;
Conference_Titel :
Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4253-4
DOI :
10.1109/ICIPS.1997.672816