Abstract
Multi-Net systems in general, and the Real Adaboost algorithm in particular, offer a very interesting way of designing very powerful classifiers. However, one inconvenient of this schemes is the large computational burden required for their construction. In this paper, we propose a new Boosting scheme which incorporates subsampling mechanisms to speed up the training of base learners and, therefore, the setup of the ensemble network. Furthermore, subsampling the training data provides additional diversity among the constituent learners, according to the some principles exploited by Bagging approaches. Experimental results show that our method is in fact able to improve both Boosting and Bagging schemes in terms of recognition rates, while allowing significant training time reductions.
This work was supported in part by the MEC Pjt. TEC2008-02473/TEC and the CM Pjt. S-0505/TIC/0223.
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Muñoz-Romero, S., Arenas-García, J., Gómez-Verdejo, V. (2009). Real Adaboost Ensembles with Emphasized Subsampling. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_55
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DOI: https://doi.org/10.1007/978-3-642-02478-8_55
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