Inducing classification rules from highly-structured data with composition

This paper elaborates on two techniques, deconstruction and composition, to handle complex data in order to learn from it. We propose typed higher-order logic as a suitable representation formalism for domains with complex structured data. Both techniques derive naturally from such framework. A naive sequential covering algorithm which uses both techniques is applied on well known learning datasets (simple and structured) to test them with good results. A further experiment on the change of knowledge representation is presented to showcase the robustness of our approach.