Simple estimators for relational Bayesian classifiers

We present the relational Bayesian classifier (RBC), a modification of the simple Bayesian classifier (SBC) for relational data. There exist several Bayesian classifiers that learn predictive models of relational data, but each uses a different estimation technique for modelling heterogeneous sets of attribute values. The effects of data characteristics on estimation have not been explored. We consider four simple estimation techniques and evaluate them on three real-world data sets. The estimator that assumes each multiset value is independently drawn from the same distribution (INDEPVAL) achieves the best empirical results. We examine bias and variance tradeoffs over a range of data sets and show that INDEPVAL's ability to model more multiset information results in lower bias estimates and contributes to its superior performance.