Discriminative vs informative learning

Abstract

The goal of pattern classification can be approached from two points of view: informative- where the classifier learns the class densities, or discriminative- where the focus is on learning the class boundaries without regard to the underlying class densities. We review and synthesize the tradeoffs between these two approaches for simple classifiers, and extend the results to modern techniques such as Naive Bayes and Generalized Additive Models. Data mining applications often operate in the domain of high dimensional features where the tradeoffs between informative and discriminative classifiers are especially relevant. Experimental results are provided for simulated and real data.