Discrimination par modèles additifs parcimonieux
We propose a nonparametric classification method designed to support the inter-pretability of the prediction. On the one hand, the use of generalized additive models makes it possible to represent the effect of each input variable on the output variable graphically. On the other hand, parameters of this model are estimated via penalized likelihood, where the term of regularization generalizes the l 1 -penalization to the splines functions. This penalization favors parsimonious solutions selecting one part of the set of input variables, while allowing a flexible modeling of the dependence on the selected variables. We study the adaptation of various analytical model selection criteria to these models, and we evaluate them on two real data sets.