Abstract
This chapter presents two different approaches for ensemble feature selection based on the filter model, aiming at achieving a good classification performance together with an important reduction in the input dimensionality. In this manner, we try to overcome the issue of selecting an appropriate method for each problem at hand, as it is usually very dependent on the characteristics of the datasets. The adequacy of using an ensemble of filters instead of a single filter is demonstrated on both synthetic and real data, including the challenging scenario of DNA microarray classification.
Part of the content of this chapter was previously published in Pattern Recognition (https://doi.org/10.1016/j.patcog.2011.06.006) and Neurocomputing (https://doi.org/10.1016/j.neucom.2013.03.067).
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Bolón-Canedo, V., Alonso-Betanzos, A. (2018). Applications of Ensembles Versus Traditional Approaches: Experimental Results. In: Recent Advances in Ensembles for Feature Selection. Intelligent Systems Reference Library, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-319-90080-3_8
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DOI: https://doi.org/10.1007/978-3-319-90080-3_8
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