Boosting Inductive Logic Programming via Decomposition, Merging, and Refinement

Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering given positive examples and excluding negative examples. It is a sub field of machine learning that uses first-order logic as a uniform representation for examples and hypothesis. In this paper we propose a method to boost given ILP learning algorithm by first decomposing the set of examples to subsets and applying the learning algorithm to each subset separately, second, merging the hypotheses obtained for subsets to get a single hypothesis for the complete set of examples, and finally refining this single hypothesis to make it shorter.