Name
Abstract | ||
|---|---|---|
Financial forecasting is a lucrative and complicated application of machine learning. In this paper, we focus on the finding investment opportunities. We therefore explore four different Genetic Programming approaches and compare their performances on real-world data. We find that the novelties we introduced in some of these approaches indeed improve the results. However, we also show that the Genetic Programming process itself is still very inefficient and that further improvements are necessary if we want this application of GP to become successful. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/COGINF.2010.5599820 | IEEE ICCI |
Keywords | Field | DocType |
auc,classification,decision rules,eddie,entropy,fgp,finance,forecasting,genetic programming,learning artificial intelligence,decision rule,evolutionary computation,measurement,accuracy,investment,genetic algorithms,machine learning,decision trees | Financial forecasting,Decision rule,Decision tree,Data set,Computer science,Evolutionary computation,Genetic programming,Artificial intelligence,Financial data processing,Genetic algorithm,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.42 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wang Pu | 1 | 66 | 3.59 |
| Edward P. K. Tsang | 2 | 899 | 87.77 |
| Weise Thomas | 3 | 682 | 44.68 |
| Tang Ke | 4 | 2798 | 139.09 |
| Xin Yao | 5 | 14858 | 945.63 |