Linear penalization support vector machines for feature selection

Authors:
Jaime Miranda;Ricardo Montoya;Richard Weber
Affiliations:
Department of Industrial Engineering, Faculty of Physical and Mathematical Sciences, University of Chile;Department of Industrial Engineering, Faculty of Physical and Mathematical Sciences, University of Chile;Department of Industrial Engineering, Faculty of Physical and Mathematical Sciences, University of Chile
Venue:
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Year:
2005

Citing 6
Cited 4

Integer and combinatorial optimization

Integer and combinatorial optimization
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Feature Selection via Concave Minimization and Support Vector Machines

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Second Order Cone Programming Formulations for Feature Selection

The Journal of Machine Learning Research
A Hybrid Forecasting Methodology using Feature Selection and Support Vector Regression

HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems

A wrapper method for feature selection using Support Vector Machines

Information Sciences: an International Journal
Simultaneous feature selection and classification using kernel-penalized support vector machines

Information Sciences: an International Journal
An efficient feature selection method for mobile devices with application to activity recognition

Neurocomputing
Integration Of Random Sample Selection, Support Vector Machines And Ensembles For Financial Risk Forecasting With An Empirical Analysis On The Necessity Of Feature Selection

International Journal of Intelligent Systems in Accounting and Finance Management

Quantified Score

Hi-index	0.00

Visualization

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.