TY - JOUR
T1 - Detecting Relevant Variables and Interactions in Supervised Classification
AU - Carrizosa, Emilio
AU - Martín-Barragán, Belén
AU - Romero Morales, Dolores
PY - 2011
Y1 - 2011
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.
KW - Supervised classification
KW - Interactions
KW - Support vector machines
KW - Binarization
U2 - 10.1016/j.ejor.2010.03.020
DO - 10.1016/j.ejor.2010.03.020
M3 - Journal article
SN - 0377-2217
VL - 213
SP - 260
EP - 269
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -