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.
Author details: | Elisa Cilia, Niels LandwehrORCiDGND, Andrea Passerini |
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DOI: | https://doi.org/10.3233/FI-2011-604 |
ISSN: | 0169-2968 |
Title of parent work (English): | Fundamenta informaticae |
Publisher: | IOS Press |
Place of publishing: | Amsterdam |
Publication type: | Article |
Language: | English |
Year of first publication: | 2011 |
Publication year: | 2011 |
Release date: | 2017/03/26 |
Volume: | 113 |
Issue: | 2 |
Number of pages: | 27 |
First page: | 151 |
Last Page: | 177 |
Funding institution: | NIH [P41 RR-01081] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
Peer review: | Referiert |
Institution name at the time of the publication: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik |