Abstract
This work analyzes the problem of whether, given a classification ensemble built by Adaboost, it is possible to find a subensemble with lower generalization error. In order to solve this task a genetic algorithm is proposed and compared with other heuristics like Kappa pruning and Reduce-error pruning with backfitting. Experiments carried out over a wide variety of classification problems show that the genetic algorithm behaves better than, or at least, as well as the best of those heuristics and that subensembles with similar and sometimes better prediction accuracy can be obtained.
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Hernández-Lobato, D., Hernández-Lobato, J.M., Ruiz-Torrubiano, R., Valle, Á. (2006). Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_39
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DOI: https://doi.org/10.1007/11875581_39
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