Name
Title | ||
|---|---|---|
Predicting cross-country results using feature selection and evolutionary computation |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1565799.1565809 | Richard Tapia Celebration of Diversity in Computing Conference, 2003 |
Keywords | Field | DocType |
feature selection,cross-country result,evolutionary computation,mainstream sport,current system,best result,particle swarm optimizer,optimal weight set,national meet result,optimal feature set,feature extraction,machine learning,evolutionary computing | Football,Feature selection,Computer science,Evolutionary computation,Feature extraction,Feature set,Artificial intelligence,Machine learning,Particle swarm optimizer | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caio Soares | 1 | 11 | 2.61 |
| Juan E. Gilbert | 2 | 170 | 44.51 |