Abstract
Although many classification methods take advantage of fuzzy sets theory, the same cannot be said for feature reduction methods. In this paper we explore ideas related to the use of fuzzy sets and we propose a novel fuzzy feature selection method tailored for the Regularized Generalized Eigenvalue Classifier (ReGEC). The method provides small and robust subsets of features that can be used for supervised classification. We show, using real world datasets that the performance of ReGEC classifier on the selected features well compares with that obtained using them all.
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Acknowledgment
This work has been partially funded by Italian Flagship project Interomics and Kauno Technologijos Universitetas (KTU).
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Ferraro, M.B., Irpino, A., Verde, R., Guarracino, M.R. (2013). A Novel Feature Selection Method for Classification Using a Fuzzy Criterion. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_49
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DOI: https://doi.org/10.1007/978-3-642-44973-4_49
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