Abstract
The paper presents the dynamic ensemble selection method. Proposed method uses information from so-called decision profiles which are formed from the outputs of the base classifiers. In order to verify these algorithms, a number of experiments have been carried out on several public available data sets. The proposed dynamic ensemble selection is experimentally compared against all base classifiers and the ensemble classifiers based on the sum and decision profile methods. As a base classifiers we used the pool of homogeneous classifiers.
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Acknowledgments
This work was supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264 and by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Technology.
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Burduk, R., Heda, P. (2017). Homogeneous Ensemble Selection - Experimental Studies. In: Kobayashi, Sy., Piegat, A., Pejaś, J., El Fray, I., Kacprzyk, J. (eds) Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. ACS 2016. Advances in Intelligent Systems and Computing, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-48429-7_6
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DOI: https://doi.org/10.1007/978-3-319-48429-7_6
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