par Cilia, Elisa 
;Landwehr, Neils;Passerini, Andrea
Référence Fundamenta informaticae, 113, 2, page (151-177)
Publication Publié, 2011
;Landwehr, Neils;Passerini, AndreaRéférence Fundamenta informaticae, 113, 2, page (151-177)
Publication Publié, 2011
Article révisé par les pairs
| Résumé : | 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. |
