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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Using Bag-of-Little Bootstraps for Efficient Ensemble Learning
verfasst von : Pablo de Viña, Gonzalo Martínez-Muñoz
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018
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Abstract
The technique bag-of-little bootstrap provides statistical estimates equivalent to the ones of bootstrap in a tiny fraction of the time required by bootstrap. In this work, we propose to combine bag-of-little bootstrap into an ensemble of classifiers composed of random trees. We show that using this bootstrapping procedure, instead of standard bootstrap samples, as the ones used in random forest, can dramatically reduce the training time of ensembles of classifiers. In addition, the experiments carried out illustrate that, for a wide range of training times, the proposed ensemble method achieves a generalization error smaller than that achieved by random forest.