Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Artificially Intelligent Sports Tipper
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Feature selection in supervised and unsupervised learning via evolutionary search
Feature selection in supervised and unsupervised learning via evolutionary search
Artificial Intelligence in Sports Prediction
ITNG '08 Proceedings of the Fifth International Conference on Information Technology: New Generations
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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Although some work has been done to better predict the outcome of sporting events, it has focused on mainstream sports such as football and has typically employed forecasting or machine learning techniques. This work focuses on the sport of cross-country, and uses feature selection and evolutionary computation to better predict National Meet results. Feature Selection is utilized to find the most optimal feature set and a Particle Swarm Optimizer (PSO) to find the most optimal weight set. The best results are attained using the PSO, with an improvement over the current system of 2.5% for Women and 0.3% for Men.