Comparison between SVM and Logistic Regression: Which One is Better to Discriminate?

Abstract

"The classifcation of individuals is a common problem in applied statistics. If X is a data set corresponding to a sample from an specifc population in which observations belong to g diffrent categories, the goal of classifcation 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 classi¿cation 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."