Name
Abstract | ||
|---|---|---|
We show how the understandability and speed of genetic programming classification algorithms can be improved, without affecting the classification accuracy. By analyzing the decision trees evolved we can remove the unessential parts, called introns, from the discovered decision trees. Since the resulting trees contain only useful information they are smaller and easier to understand. Moreover, by using these pruned decision trees in a fitness cache we can significantly reduce the number of unnecessary fitness calculations. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30217-9_108 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
decision tree | Decision tree,Computer science,CPU cache,Cache,Tree (data structure),Genetic programming,Artificial intelligence,Statistical classification,Symbolic regression,Machine learning,Genetic algorithm | Conference |
Volume | ISSN | Citations |
3242 | 0302-9743 | 7 |
PageRank | References | Authors |
0.68 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeroen Eggermont | 1 | 211 | 17.08 |
| Joost N. Kok | 2 | 1429 | 121.49 |
| Walter A. Kosters | 3 | 310 | 32.97 |