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http://dx.doi.org/10.7232/IEIF.2011.24.2.151

Signomial Classification Method with 0-regularization  

Lee, Kyung-Sik (Department of Industrial and Management Engineering, Hankuk University of Foreign Studies)
Publication Information
IE interfaces / v.24, no.2, 2011 , pp. 151-155 More about this Journal
Abstract
In this study, we propose a signomial classification method with 0-regularization (0-)which seeks a sparse signomial function by solving a mixed-integer program to minimize the weighted sum of the 0-norm of the coefficient vector of the resulting function and the $L_1$-norm of loss caused by the function. $SC_0$ gives an explicit description of the resulting function with a small number of terms in the original input space, which can be used for prediction purposes as well as interpretation purposes. We present a practical implementation of $SC_0$ based on the mixed-integer programming and the column generation procedure previously proposed for the signomial classification method with $SL_1$-regularization. Computational study shows that $SC_0$ gives competitive performance compared to other widely used learning methods for classification.
Keywords
classification; signomial function; 0-regularization; mixed-integer program; column generation;
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Times Cited By KSCI : 1  (Citation Analysis)
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