Many are called, but few are chosen. Feature selection and error estimation in high dimensional spaces.

We address the problems of feature selection and error estimation when the number of possible feature candidates is large and the number of training samples is limited. A Monte Carlo study has been performed to illustrate the problems when using stepwise feature selection and discriminant analysis. The simulations demonstrate that in order to find the correct features, the necessary ratio of number of training samples to feature candidates is not a constant.