  Learning the Markov network structure from data is a problem that has
received considerable attention in machine learning, and in many other
application fields. This work focuses on a particular approach for this purpose
called independence-based learning. Such approach guarantees the learning of
the correct structure efficiently, whenever data is sufficient for representing
the underlying distribution. However, an important issue of such approach is
that the learned structures are encoded in an undirected graph. The problem
with graphs is that they cannot encode some types of independence relations,
such as the context-specific independences. They are a particular case of
conditional independences that is true only for a certain assignment of its
conditioning set, in contrast to conditional independences that must hold for
all its assignments. In this work we present CSPC, an independence-based
algorithm for learning structures that encode context-specific independences,
and encoding them in a log-linear model, instead of a graph. The central idea
of CSPC is combining the theoretical guarantees provided by the
independence-based approach with the benefits of representing complex
structures by using features in a log-linear model. We present experiments in a
synthetic case, showing that CSPC is more accurate than the state-of-the-art IB
algorithms when the underlying distribution contains CSIs.
