First order random forests with complex aggregates

Random forest induction is a bagging method that randomly samples the feature set at each node in a decision tree. In propositional learning, the method has been shown to work well when lots of features are available. This certainly is the case in first order learning, especially when aggregate functions, combined with selection conditions on the set to be aggregated, are included in the feature space. In this paper, we introduce a random forest based approach to learning first order theories with aggregates. We experimentally validate and compare several variants: first order random forests without aggregates, with simple aggregates, and with complex aggregates in the feature set.