TY  - JOUR
A1  - Cilia, Elisa
A1  - Landwehr, Niels
A1  - Passerini, Andrea
T1  - Relational feature mining with hierarchical multitask kFOIL
JF  - Fundamenta informaticae
N2  - 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.
Y1  - 2011
U6  - https://doi.org/10.3233/FI-2011-604
SN  - 0169-2968
VL  - 113
IS  - 2
SP  - 151
EP  - 177
PB  - IOS Press
CY  - Amsterdam
ER  -