Gradient-based boosting for statistical relational learning: The relational dependency network case

Authors:
Sriraam Natarajan;Tushar Khot;Kristian Kersting;Bernd Gutmann;Jude Shavlik
Affiliations:
School of Medicine, Wake Forest University, Winston Salem, USA;University of Wisconsin-Madison, Madison, USA;Frauhofer IAIS, Sankt Augustin, Germany;K.U. Leuven, Leuven, Belgium;University of Wisconsin-Madison, Madison, USA
Venue:
Machine Learning
Year:
2012

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Abstract

Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, comes at the expense of a more complex model-selection problem: an unbounded number of relational abstraction levels might need to be explored. Whereas current learning approaches for RDNs learn a single probability tree per random variable, we propose to turn the problem into a series of relational function-approximation problems using gradient-based boosting. In doing so, one can easily induce highly complex features over several iterations and in turn estimate quickly a very expressive model. Our experimental results in several different data sets show that this boosting method results in efficient learning of RDNs when compared to state-of-the-art statistical relational learning approaches.