Two-way Parallel Class Expression Learning


A.C. Tran, J. Dietrich, H.W. Guesgen, S. Marsland ;
Proceedings of the Asian Conference on Machine Learning, PMLR 25:443-458, 2012.


In machine learning, we often encounter datasets that can be described using simple rules and regular exception patterns describing situations where those rules do not apply. In this paper, we propose a two-way parallel class expression learning algorithm that is suitable for this kind of problem. This is a top-down refinement-based class expression learning algorithm for Description Logic (DL). It is distinguished from similar DL learning algorithms in the way it uses the concepts generated by the refinement operator. In our approach, we unify the computation of concepts describing positive and negative examples, but we maintain them separately, and combine them at the end. By doing so, we can avoid the use of negation in the refinement without any loss of generality. Evaluation shows that our approach can reduce the search space significantly, and therefore the learning time is reduced. Our implementation is based on the DL-Learner framework and we inherit the Parallel Class Expression Learning (ParCEL) algorithm design for parallelisation.

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