Name
Abstract | ||
|---|---|---|
Decision trees have three main disadvantages: reduced performance when the training set is small; rigid decision criteria; and the fact that a single “uncharacteristic” attribute might “derail” the classification process. In this paper we present ConfDTree (Confidence-Based Decision Tree) — a post-processing method that enables decision trees to better classify outlier instances. This method, which can be applied to any decision tree algorithm, uses easy-to-implement statistical methods (confidence intervals and two-proportion tests) in order to identify hard-to-classify instances and to propose alternative routes. The experimental study indicates that the proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class datasets in which an average improvement of 5%~9% in the AUC performance is reported. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s11390-014-1438-5 | J. Comput. Sci. Technol. |
Keywords | Field | DocType |
decision tree, confidence interval, imbalanced dataset | Data mining,Decision tree,Multiple-criteria decision analysis,Computer science,Outlier,Artificial intelligence,Confidence interval,ID3 algorithm,Alternating decision tree,Machine learning,Decision tree learning,Incremental decision tree | Journal |
Volume | Issue | ISSN |
29 | 3 | 1860-4749 |
Citations | PageRank | References |
0 | 0.34 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gilad Katz | 1 | 106 | 15.61 |
| Asaf Shabtai | 2 | 1176 | 100.03 |
| Lior Rokach | 3 | 2127 | 142.59 |
| Nir Ofek | 4 | 80 | 7.69 |