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

doi:10.1007/s10994-011-5244-9

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

2011-05-10

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DOI：10.1007/s10994-011-5244-9

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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.

Keywords:

Statistical relational learning

Graphical models

Ensemble methods

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

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