Relational Feature Mining with Hierarchical Multitask kFOIL
Fundam. Inform.(2011)
摘要
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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关键词
multi task alternative,drug-resistance mutation prediction,relational feature mining,core logic representation,hierarchical kfoil,hierarchical version,task hierarchy,hierarchical multitask kfoil,deeper hierarchy,multitask kfoil,task clustering,relational learning
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