Supervised classification is one of the most used methods in machine learning. In case of data characterized by a large number of features, a critical issue is to deal with redundant or irrelevant information. To this extent, an effective algorithm needs to identify a suitable subset of features, as small as possible, for the classification. In this work we present ReGEC_L1, a classifier with embedded feature selection based on the Regularized Generalized Eigenvalue Classifier (ReGEC) and equipped with a L1-norm regularization term. We detail the mathematical formulation and the numerical algorithm. Numerical results, obtained on some de facto standard benchmark data sets, show that the approach we propose produces a remarkable selection of the features, without losing accuracy in the classification. In that respect, our algorithm seems to compare favorably with the SVM_L1 method. A MATLAB implementation of ReGEC_L1 is available at http://​www.​na.​icar.​cnr.​it/​~mariog/​regec_​l1.​html.

A generalized eigenvalues classifier with embedded feature selection / Viola, Marco; Sangiovanni, Mara; Toraldo, Gerardo; Guarracino, Mario R.. - In: OPTIMIZATION LETTERS. - ISSN 1862-4472. - 11:2(2017), pp. 299-311. [10.1007/s11590-015-0955-7]

A generalized eigenvalues classifier with embedded feature selection

VIOLA, MARCO;SANGIOVANNI, Mara;TORALDO, GERARDO;
2017

Abstract

Supervised classification is one of the most used methods in machine learning. In case of data characterized by a large number of features, a critical issue is to deal with redundant or irrelevant information. To this extent, an effective algorithm needs to identify a suitable subset of features, as small as possible, for the classification. In this work we present ReGEC_L1, a classifier with embedded feature selection based on the Regularized Generalized Eigenvalue Classifier (ReGEC) and equipped with a L1-norm regularization term. We detail the mathematical formulation and the numerical algorithm. Numerical results, obtained on some de facto standard benchmark data sets, show that the approach we propose produces a remarkable selection of the features, without losing accuracy in the classification. In that respect, our algorithm seems to compare favorably with the SVM_L1 method. A MATLAB implementation of ReGEC_L1 is available at http://​www.​na.​icar.​cnr.​it/​~mariog/​regec_​l1.​html.
2017
A generalized eigenvalues classifier with embedded feature selection / Viola, Marco; Sangiovanni, Mara; Toraldo, Gerardo; Guarracino, Mario R.. - In: OPTIMIZATION LETTERS. - ISSN 1862-4472. - 11:2(2017), pp. 299-311. [10.1007/s11590-015-0955-7]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/622828
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