Bayesian Support Vector Machines for Feature Ranking and Selection

In this chapter, we develop and evaluate a feature selection algorithm for Bayesian support vector machines. The relevance level of features are represented by ARD (automatic relevance determination) parameters, which are optimized by maximizing the model evidence in the Bayesian framework. The features are ranked in descending order using the optimal ARD values, and then forward selection is carried out to determine the minimal set of relevant features. In the numerical experiments, our approach using ARD for feature ranking can achieve a more compact feature set than standard ranking techniques, along with better generalization performance.