An Information Criterion for Variable Selection in Support Vector Machines

Using support vector machines for classification problems has the advantage that the curse of dimensionality is circumvented. However, it has been shown that even here a reduction of the dimension of the input space leads to better results. For this purpose, we propose two information criteria which can be computed directly from the definition of the support vector machine. We assess the predictive performance of the models selected by our new criteria and compare them to a few existing variable selection techniques in a simulation study. Results of this simulation study show that the new criteria are very competitive compared to the others in terms of out-of-sample error rate while being much easier to compute. When we repeat this comparison on a few real-world benchmark datasets, we arrive at the same findings