ECML PKDD edition:2015 location:Porto, Portugal date:7-11 September 2015
Statistical Relational Learning (SRL) is concerned with developing formalisms
for representing and learning from data that exhibit both uncertainty and
complex, relational structure. Most of the work in SRL has focused on modeling
and learning from data that only contain discrete variables. As many important
problems are characterized by the presence of both continuous and discrete variables,
there has been a growing interest in developing hybrid SRL formalisms.
Most of these formalisms focus on reasoning and representational issues and, in
some cases, parameter learning. What has received little attention is learning the
structure of a hybrid SRL model from data. In this paper, we fill that gap and make
the following contributions. First, we propose Hybrid Relational Dependency Networks
(HRDNs), an extension to Relational Dependency Networks that are able to
model continuous variables. Second, we propose an algorithm for learning both the
structure and parameters of an HRDN from data. Third, we provide an empirical
evaluation that demonstrates that explicitly modeling continuous variables results
in more accurate learned models than discretizing them prior to learning.