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Binarized Support Vector Machines

Departamento de Estadística e Investigación Operativa, Universidad de Sevilla, 41012 Sevilla, Spain
Departamento de Estadística, Universidad Carlos III de Madrid, 28903 Getafe, Madrid (Spain)
Saïd Business School, University of Oxford, Oxford OX1 1HP, United Kingdom
ecarrizosa{at}us.es
belen.martin{at}uc3m.es
dolores.romero-morales{at}sbs.ox.ac.uk

The widely used support vector machine (SVM) method has shown to yield very good results in supervised classification problems. Other methods such as classification trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in data mining.

In this work, we propose an SVM-based method that automatically detects the most important predictor variables and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals that are critical for the classification. The method involves the optimization of a linear programming problem in the spirit of the Lasso method with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard column generation strategy leads to a classification method that, in terms of classification ability, is competitive against the standard linear SVM and classification trees. Moreover, the proposed method is robust; i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables.

When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler but still competitive classifiers.

Key words: supervised classification; binarization; column generation; support vector machines
History: received February 2006; revised July 2008; accepted December 2008.