Name
Abstract | ||
|---|---|---|
The diversity of an ensemble of classifiers can be calculated in a variety of ways. Here a diversity metric and a means for altering the diversity of an ensemble, called “thinning”, are introduced. We evaluate thinning algorithms created by several techniques on 22 publicly available datasets. When compared to other methods, our percentage correct diversity measure shows a greatest correlation between the increase in voted ensemble accuracy and the diversity value. Also, the analysis of different ensemble creation methods indicates that they generate different levels of diversity. Finally, the methods proposed for thinning show that ensembles can be made smaller without loss in accuracy. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.inffus.2004.04.005 | Information Fusion |
Keywords | Field | DocType |
Thinning,Diversity,Multiple classifier systems,Decision trees,Ensembles | Decision tree,Ensemble diversity,Diversity measure,Pattern recognition,Thinning,Correlation,Artificial intelligence,Mathematics,Machine learning,Thinning algorithm | Journal |
Volume | Issue | ISSN |
6 | 1 | 1566-2535 |
Citations | PageRank | References |
108 | 2.66 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert E. Banfield | 1 | 358 | 17.16 |
| Lawrence O. Hall | 2 | 5543 | 335.87 |
| Kevin W. Bowyer | 3 | 11121 | 734.33 |
| W. Philip Kegelmeyer | 4 | 3498 | 146.54 |