Name
Abstract | ||
|---|---|---|
Several pruning strategies that can be used to reduce the size and increase the accuracy of bagging ensembles are analyzed. These heuristics select subsets of complementary classifiers that, when combined, can perform better than the whole ensemble. The pruning methods investigated are based on modifying the order of aggregation of classifiers in the ensemble. In the original bagging algorithm, the order of aggregation is left unspecified. When this order is random, the generalization error typically decreases as the number of classifiers in the ensemble increases. If an appropriate ordering for the aggregation process is devised, the generalization error reaches a minimum at intermediate numbers of classifiers. This minimum lies below the asymptotic error of bagging. Pruned ensembles are obtained by retaining a fraction of the classifiers in the ordered ensemble. The performance of these pruned ensembles is evaluated in several benchmark classification tasks under different training conditions. The results of this empirical investigation show that ordered aggregation can be used for the efficient generation of pruned ensembles that are competitive, in terms of performance and robustness of classification, with computationally more costly methods that directly select optimal or near-optimal subensembles. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TPAMI.2008.78 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
heuristics select subsets,ordered aggregation,benchmark classification task,aggregation process,generalization error,ensemble pruning,ensemble increase,asymptotic error,original bagging algorithm,bagging ensemble,pruned ensemble,whole ensemble,decision tree,learning artificial intelligence,decision trees,bagging,boosting,aggregation,set theory,sampling methods,training data,fluctuations | Set theory,Decision tree,Ensembles of classifiers,Pattern recognition,Random subspace method,Computer science,Robustness (computer science),Heuristics,Artificial intelligence,Boosting (machine learning),Pruning | Journal |
Volume | Issue | ISSN |
31 | 2 | 0162-8828 |
Citations | PageRank | References |
147 | 3.50 | 39 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gonzalo Martínez-Muñoz | 1 | 524 | 23.76 |
| Daniel Hernández-Lobato | 2 | 440 | 26.10 |
| Alberto Suárez | 3 | 487 | 22.33 |