Binarized Support Vector Machines

Emilio Carrizosa, Belén Martín-Barragán, Dolores Romero Morales

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.
Original languageEnglish
JournalI N F O R M S Journal on Computing
Volume22
Issue number1
Pages (from-to)154-167
ISSN1091-9856
DOIs
Publication statusPublished - 2010
Externally publishedYes

Cite this

Carrizosa, Emilio ; Martín-Barragán, Belén ; Romero Morales, Dolores . / Binarized Support Vector Machines. In: I N F O R M S Journal on Computing. 2010 ; Vol. 22, No. 1. pp. 154-167.
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Binarized Support Vector Machines. / Carrizosa, Emilio; Martín-Barragán, Belén; Romero Morales, Dolores .

In: I N F O R M S Journal on Computing, Vol. 22, No. 1, 2010, p. 154-167.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Binarized Support Vector Machines

AU - Carrizosa, Emilio

AU - Martín-Barragán, Belén

AU - Romero Morales, Dolores

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - Supervised classification

KW - Binarization

KW - Column generation

KW - Support vector machines

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JO - I N F O R M S Journal on Computing

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