  This article focuses on the question of learning how to automatically select
a subset of items among a bigger set. We introduce a methodology for the
inference of ensembles of discrete values, based on the Naive Bayes assumption.
Our motivation stems from practical use cases where one wishes to predict an
unordered set of (possibly interdependent) values from a set of observed
features. This problem can be considered in the context of Multi-label
Classification (MLC) where such values are seen as labels associated to
continuous or discrete features. We introduce the \nbx algorithm, an extension
of Naive Bayes classification into the multi-label domain, discuss its
properties and evaluate our approach on real-world problems.
