Chapter

Intelligent Data Engineering and Automated Learning – IDEAL 2006

Volume 4224 of the series Lecture Notes in Computer Science pp 322-329

Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm

  • Daniel Hernández-LobatoAffiliated withCarnegie Mellon UniversityEscuela Politécnica Superior, Universidad Autónoma de Madrid
  • , José Miguel Hernández-LobatoAffiliated withCarnegie Mellon UniversityEscuela Politécnica Superior, Universidad Autónoma de Madrid
  • , Rubén Ruiz-TorrubianoAffiliated withCarnegie Mellon UniversityEscuela Politécnica Superior, Universidad Autónoma de Madrid
  • , Ángel ValleAffiliated withCarnegie Mellon UniversityCognodata

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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.