Abstract
Support Vector Machines have proved to be powerful tools for classification tasks combining the minimization of classification errors and maximizing their generalization capabilities. Feature selection, however, is not considered explicitly in the basic model formulation. We propose a linearly penalized Support Vector Machines (LP-SVM) model where feature selection is performed simultaneously with model construction. Its application to a problem of customer retention and a comparison with other feature selection techniques demonstrates its effectiveness.
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Keywords
- Support Vector Machine
- Feature Selection
- Current Account
- Support Vector Regression
- Feature Selection Technique
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Miranda, J., Montoya, R., Weber, R. (2005). Linear Penalization Support Vector Machines for Feature Selection. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_24
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DOI: https://doi.org/10.1007/11590316_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
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