A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to variable and feature selection
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Training a Support Vector Machine in the Primal
Neural Computation
Bioinformatics
Feature words that classify problem sentence in scientific article
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
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Most Support Vector Machines (SVM) implementations are based on solving the dual optimization problem. Of course, feature selection algorithms based on SVM are not different and, in particular, the most used method in the area, Guyon et al.'s Recursive Feature Elimination (SVM-RFE) is also based on the dual problem. However, this is just one of the options available to find a solution to the original SVM optimization problem. In this work we discuss some potential problems that arise when ranking features with the dual-based version of SVM-RFE and propose a primal-based version of this well-known method, PSVM-RFE. We show that our new method is able to produce a better detection of relevant features, in particular in situations involving non-linear decision boundaries. Using several artificial and real-world datasets we compare both versions of SVM-RFE, finding that PSVM-RFE is preferable in most situations.