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Relational feature mining with hierarchical multitask kFOIL

  • 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.

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Metadaten
Author:Elisa Cilia, Niels Landwehr, Andrea Passerini
DOI:https://doi.org/10.3233/FI-2011-604
ISSN:0169-2968 (print)
Parent Title (English):Fundamenta informaticae
Publisher:IOS Press
Place of publication:Amsterdam
Document Type:Article
Language:English
Year of first Publication:2011
Year of Completion:2011
Release Date:2017/03/26
Volume:113
Issue:2
Pagenumber:27
First Page:151
Last Page:177
Funder:NIH [P41 RR-01081]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
Peer Review:Referiert
Institution name at the time of publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik