  The purpose of this study is to introduce a new approach to feature ranking
for classification tasks, called in what follows greedy feature selection. In
statistical learning, feature selection is usually realized by means of methods
that are independent of the classifier applied to perform the prediction using
that reduced number of features. Instead, greedy feature selection identifies
the most important feature at each step and according to the selected
classifier. In the paper, the benefits of such scheme are investigated
theoretically in terms of model capacity indicators, such as the
Vapnik-Chervonenkis (VC) dimension or the kernel alignment, and tested
numerically by considering its application to the problem of predicting
geo-effective manifestations of the active Sun.
