Detecting relevant variables and interactions in supervised classification

E. Carrizosa, B. Martín-Barragán, D.R. Morales

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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
Pages (from-to)260-269
Number of pages10
JournalEuropean Journal of Operational Research
Issue number1
Publication statusPublished - 16 Aug 2011

Keywords / Materials (for Non-textual outputs)

  • Supervised classification
  • Interactions
  • Support vector machines
  • Binarization


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