Article
Detecting relevant variables and interactions in supervised classification
Author/s | Carrizosa Priego, Emilio José
![]() ![]() ![]() ![]() ![]() ![]() ![]() Martín Barragán, Belén Romero Morales, María Dolores |
Department | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Publication Date | 2011-08-16 |
Deposit Date | 2016-09-08 |
Published in |
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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 ... 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. |
Funding agencies | Ministerio de Educación y Ciencia (MEC). España Junta de Andalucía |
Project ID. | MTM2009-14039
![]() ECO2008-05080 ![]() FQM-329 ![]() |
Citation | 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. |
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