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Detecting relevant variables and interactions in supervised classification

 

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Opened Access Detecting relevant variables and interactions in supervised classification
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Author: Carrizosa Priego, Emilio José
Martín Barragán, Belén
Romero Morales, María Dolores
Department: Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
Date: 2011-08-16
Published in: European Journal of Operational Research, 213 (1), 260-269.
Document type: Article
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 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.
Cite: Carrizosa Priego, E.J., Martín Barragán, B. y Romero Morales, M.D. (2011). Detecting relevant variables and interactions in supervised classification. European Journal of Operational Research, 213 (1), 260-269.
Size: 485.4Kb
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URI: http://hdl.handle.net/11441/44822

DOI: 10.1016/j.ejor.2010.03.020

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