Services on Demand
Journal
Article
Indicators
- Cited by SciELO
- Access statistics
Related links
- Cited by Google
- Similars in SciELO
- Similars in Google
Share
Revista Colombiana de Estadística
Print version ISSN 0120-1751
Abstract
SALAZAR, DIEGO ALEJANDRO; VELEZ, JORGE IVÁN and SALAZAR, JUAN CARLOS. Comparison between SVM and Logistic Regression: Which One is Better to Discriminate?. Rev.Colomb.Estad. [online]. 2012, vol.35, n.spe2, pp.223-237. ISSN 0120-1751.
The classification of individuals is a common problem in applied statistics. If X is a data set corresponding to a sample from an specific population in which observations belong to g different categories, the goal of classification methods is to determine to which of them a new observation will belong to. When g=2, logistic regression (LR) is one of the most widely used classification methods. More recently, Support Vector Machines (SVM) has become an important alternative. In this paper, the fundamentals of LR and SVM are described, and the question of which one is better to discriminate is addressed using statistical simulation. An application with real data from a microarray experiment is presented as illustration.
Keywords : Classification; Genetics; Logistic regression; Simulation; Support vector machines.