Name
Abstract | ||
|---|---|---|
Many existing constructive decision tree learning algorithms such as Fringe and Citre construct conjunctions or disjunctions directly from paths of decision trees. This paper investigates a novel attribute construction method for decision tree learning It creates conjunctions from production rules that are transformed from decision trees. Irrelevant or unimportant conditions are eliminated when paths are transformed into production rules. Therefore, this new method is likely to construct new attributes with relevant conditions. Three constructive induction algorithms based on this basic idea are described and are empirically evaluated by comparing with C4.5 and a Fringe-like algorithm in a set of artificial and natural domains. The experimental results reveal that constructing conjunctions using production rules can significantly improve the performance of decision tree learning in the majority of the domains tested in terms of both higher prediction accuracy and lower theory complexity. These results suggest an advantage of the attribute construction method that uses production rules over the method of constructing new attributes directly from paths in noisy domains. |
Year | Venue | Keywords |
|---|---|---|
2000 | JOURNAL OF RESEARCH AND PRACTICE IN INFORMATION TECHNOLOGY | constructive induction,decision tree learning,classification,machine learning |
DocType | Volume | Issue |
Journal | 32 | 1 |
ISSN | Citations | PageRank |
1443-458X | 1 | 0.38 |
References | Authors | |
5 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zijian Zhang | 1 | 27 | 9.14 |