Detecting Relevant Variables and Interactions in Supervised Classification

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 good results in Supervised Classification problems. When the interpretability is an important issue, then classification methods such as Classification and Regression Trees (CART) might be more attractive, since they are designed to detect the important predictor variables and, for each predictor variable, the critical values which are most relevant for classification. However, when interactions between variables strongly affect the class membership, CART may yield misleading information. Extending previous work of the authors, in this paper an SVM-based method is introduced. The numerical experiments reported show that our method is competitive against SVM and CART in terms of misclassification rates, and, at the same time, is able to detect critical values and variables interactions which are relevant for classification.
Original languageEnglish
JournalEuropean Journal of Operational Research
Volume213
Issue number1
Pages (from-to)260–269
ISSN0377-2217
DOIs
Publication statusPublished - 2011
Externally publishedYes

Cite this

Carrizosa, Emilio ; Martín-Barragán, Belén ; Romero Morales, Dolores . / Detecting Relevant Variables and Interactions in Supervised Classification. In: European Journal of Operational Research. 2011 ; Vol. 213, No. 1. pp. 260–269.
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Detecting Relevant Variables and Interactions in Supervised Classification. / Carrizosa, Emilio; Martín-Barragán, Belén; Romero Morales, Dolores .

In: European Journal of Operational Research, Vol. 213, No. 1, 2011, p. 260–269.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Detecting Relevant Variables and Interactions in Supervised Classification

AU - Carrizosa, Emilio

AU - Martín-Barragán, Belén

AU - Romero Morales, Dolores

PY - 2011

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N2 - The widely used Support Vector Machine (SVM) method has shown to yield good results in Supervised Classification problems. When the interpretability is an important issue, then classification methods such as Classification and Regression Trees (CART) might be more attractive, since they are designed to detect the important predictor variables and, for each predictor variable, the critical values which are most relevant for classification. However, when interactions between variables strongly affect the class membership, CART may yield misleading information. Extending previous work of the authors, in this paper an SVM-based method is introduced. The numerical experiments reported show that our method is competitive against SVM and CART in terms of misclassification rates, and, at the same time, is able to detect critical values and variables interactions which are relevant for classification.

AB - The widely used Support Vector Machine (SVM) method has shown to yield good results in Supervised Classification problems. When the interpretability is an important issue, then classification methods such as Classification and Regression Trees (CART) might be more attractive, since they are designed to detect the important predictor variables and, for each predictor variable, the critical values which are most relevant for classification. However, when interactions between variables strongly affect the class membership, CART may yield misleading information. Extending previous work of the authors, in this paper an SVM-based method is introduced. The numerical experiments reported show that our method is competitive against SVM and CART in terms of misclassification rates, and, at the same time, is able to detect critical values and variables interactions which are relevant for classification.

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KW - Interactions

KW - Support vector machines

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