Feature Ranking Using Linear SVM
Yin-Wen Chang and Chih-Jen Lin; JMLR W&CP 3:53-64, 2008.
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 difference 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 effective on data sets in the Causality
Challenge.
