Feature Ranking Using Linear SVM

Abstract

Feature ranking is useful to gain knowledge of data and identify relevant features. This article explores the performance of combining linear support vector machines with various feature ranking methods, and reports the experiments conducted when participating the Causality Challenge. Experiments show that a feature ranking using weights from linear SVM models yields good performances, even when the training and testing data are not identically distributed. Checking the dierence of Area Under Curve (AUC) with and without removing each feature also gives similar rankings. Our study indicates that linear SVMs with simple feature rankings are eective on data sets in the Causality Challenge.