To Örebro University

Regularization is one of the key concepts in machine learning, but so far it has received only little attention in the logical and relational learning setting. Here we propose a regularization and feature selection technique for such setting, in which one commonly represents the structure of the domain using an entity-relationship model. To this end, we introduce a notion of locality that ties together features according to their proximity in a transformed representation of the relational learning problem obtained via a procedure that we call “graphicalization”. We present two techniques, a wrapper and an efficient embedded approach, to identify the most relevant sets of predicates which yields more readily interpretable results than selecting low-level propositionalized features. The proposed techniques are implemented in the kernel-based relational learner kLog, although the ideas presented here can also be adapted to other relational learning frameworks. We evaluate our approach on classification tasks in the natural language processing and bioinformatics domain.