Relational Feature Mining with Hierarchical Multitask kFOIL

Issue title: Machine Learning in Bioinformatics

Article type: Research Article

Authors: Cilia, Elisa | Landwehr, Niels | Passerini, Andrea

Affiliations: Département d'Informatique, Université Libre de Bruxelles, Belgium. ecilia@ulb.ac.be | Department of Computer Science, University of Potsdam, Germany. landwehr@cs.uni-potsdam.de | Dipartimento di Ingegneria e Scienza dell'Informazione, University of Trento, Italy. passerini@disi.unitn.it

Note: [] Address for correspondence: Département d'Informatique, Université Libre de Bruxelles, Belgium This work was done while she was at Dipartimento di Ingegneria e Scienza dell'Informazione, University of Trento, Italy

Abstract: We introduce hierarchical kFOIL as a simple extension of the multitask kFOIL learning algorithm. The algorithm first learns a core logic representation common to all tasks, and then refines it by specialization on a per-task basis. The approach can be easily generalized to a deeper hierarchy of tasks. A task clustering algorithm is also proposed in order to automatically generate the task hierarchy. The approach is validated on problems of drug-resistance mutation prediction and protein structural classification. Experimental results show the advantage of the hierarchical version over both single and multi task alternatives and its potential usefulness in providing explanatory features for the domain. Task clustering allows to further improve performance when a deeper hierarchy is considered.

DOI: 10.3233/FI-2011-604

Journal: Fundamenta Informaticae, vol. 113, no. 2, pp. 151-177, 2011