Ensemble Learning for Free with Evolutionary Algorithms ?
| dc.creator | Gagné, Christian | |
| dc.creator | Sebag, Michèle | |
| dc.creator | Schoenauer, Marc | |
| dc.creator | Tomassini, Marco | |
| dc.date | 2007-04-30 | |
| dc.date.accessioned | 2026-07-07T07:58:45Z | |
| dc.date.available | 2026-07-07T07:58:45Z | |
| dc.description | Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-line) or incrementally along evolution (On-line). Experiments on a set of benchmark problems show that Off-line outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles. | |
| dc.identifier | https://arxiv.org/abs/0704.3905 | |
| dc.identifier | http://arxiv.org/abs/0704.3905 | |
| dc.identifier | Dans GECCO (2007) | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/128121 | |
| dc.subject | Artificial Intelligence | |
| dc.title | Ensemble Learning for Free with Evolutionary Algorithms ? | |
| dc.type | text |