Binarized support vector machines
The widely used support vector machine (SVM) method has shown to yield very good results in supervised classification problems. Other methods such as classification trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in data mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals that are critical for the classification. The method involves the optimization of a linear programming problem in the spirit of the Lasso method with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard column generation strategy leads to a classification method that, in terms of classification ability, is competitive against the standard linear SVM and classification trees. Moreover, the proposed method is robust; i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler but still competitive classifiers.
Year of publication: |
2010-12
|
---|---|
Authors: | Carrizosa, Emilio ; Martin-Barragan, Belen ; Romero-Morales, Dolores |
Saved in:
Online Resource
Type of publication: | Article |
---|---|
Language: | English |
Notes: | Carrizosa, Emilio, Martin-Barragan, Belen and Romero-Morales, Dolores (2010) Binarized support vector machines. INFORMS Journal on Computing, 22 (1). pp. 154-167. |
Other identifiers: | 10.1287/ijoc.1090.0317 [DOI] |
Source: | BASE |
Persistent link: https://www.econbiz.de/10011425791
Saved in favorites
Similar items by person
-
Multi-group Support Vector Machines with measurement costs: a biobjective approach
Carrizosa, Emilio, (2008)
-
On the selection of the globally optimal prototype subset for Nearest-Neighbor classification
Carrizosa, Emilio, (2007)
-
A nested heuristic for parameter tuning in Support Vector Machines
Carrizosa, Emilio, (2014)
- More ...