Name
Abstract | ||
|---|---|---|
We experimentally evaluate randomization-based approachesto creating an ensemble of decision-tree classifiers.Unlike methods related to boosting, all of the eightapproaches considered here create each classifier in an ensembleindependently of the other classifiers. Experimentswere performed on 28 publicly available datasets, usingC4.5 release 8 as the base classifier. While each of the otherseven approaches has some strengths, we find that none ofthem is consistently more accurate than standard baggingwhen tested for statistical significance. |
Year | Venue | Keywords |
|---|---|---|
2003 | ICDM | otherseven approach,base classifier,standard baggingwhen,available datasets,decision-tree classifier,pure parallel ensemble creation,statistical significance,randomization-based approachesto,random processes,learning artificial intelligence,decision tree,decision trees,statistical testing,bagging |
Field | DocType | ISBN |
Data mining,Decision tree,Computer science,Bootstrap aggregating,Artificial intelligence,Classifier (linguistics),Random forest,Statistical hypothesis testing,Alternating decision tree,Pattern recognition,Boosting (machine learning),Machine learning,Gradient boosting | Conference | 0-7695-1978-4 |
Citations | PageRank | References |
15 | 1.20 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lawrence O. Hall | 1 | 5543 | 335.87 |
| Kevin W. Bowyer | 2 | 11121 | 734.33 |
| Robert E. Banfield | 3 | 358 | 17.16 |
| Divya Bhadoria | 4 | 44 | 3.83 |
| W. Philip Kegelmeyer | 5 | 3498 | 146.54 |
| Steven Eschrich | 6 | 89 | 10.81 |