Top-Down Pruning in Relational Learning

Pruning is an eeective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the eld of Inductive Logic Programming. However, it has been shown that these methods can be very ineecient, because most of the time is wasted for generating clauses that explain noisy examples and subsequently pruning these clauses. We introduce a new method which searches for good theories in a top-down fashion to get a better starting point for the pruning algorithm. Experiments show that this approach can signiicantly lower the complexity of the task as well as increase predictive accuracy.