Ensemble learning consists of generating a collection of classifiers whose predictions are then combined to yield a single unified decision. Ensembles of complementary classifiers provide accurate and robust predictions, which are often better than the predictions of the individual classifiers in the ensemble. Nevertheless, ensembles also have some drawbacks: typically, all classifiers are queried to compute the final ensemble prediction. Therefore, all the classifiers need to be accessible to address potential queries. This entails larger storage requirements and slower predictions than a single classifier. Ensemble pruning techniques are useful to alleviate these drawbacks. Static pruning techniques reduce the ensemble size by selecting a sub-ensemble of classifiers from the original ensemble. In dynamic pruning, the querying process is halted when the partial ensemble prediction is sufficient to reach a stable final decision with a reasonable amount of confidence. In this paper, we present the results of a comprehensive analysis of static and dynamic pruning techniques applied to Adaboost ensembles. These ensemble pruning techniques are evaluated on a wide range of classification problems. From this analysis, one concludes that the combination of static and dynamic pruning techniques provides a notable reduction in the memory requirements and an improvement in the classification time without a significant loss of prediction accuracy.
Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. Recent work on decision tree pruning [1] has brought to the attention of the machine learning community the fact that, in classification problems, the use of subadditive penalties in cost-complexity pruning has a stronger theoretical basis than the usual additive penalty terms. We implement cost-complexity pruning algorithms with general size-dependent penalties to confirm the results of [1]. Namely, that the family of pruned subtrees selected by pruning with a subadditive penalty of increasing strength is a subset of the family selected using additive penalties. Consequently, this family of pruned trees is unique, it is nested and it can be computed efficiently. However, in spite of the better theoretical grounding of cost-complexity pruning with subadditive penalties, we found no systematic improvements in the generalization performance of the final classification tree selected by cross-validation using subadditive penalties instead of the commonly used additive ones.
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