Publication at Faculty of Mathematics and Physics |
2012
Abstract
Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples. It uses first-order logic as a uniform representation for examples and hypothesis.
In this paper we propose a method to boost any 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 the subsets to get a single hypothesis for the complete set of examples, and finally refining this single hypothesis to make it shorter. The proposed technique significantly outperforms existing approaches.