Relational Learning Using Constrained Confidence-Rated Boosting

Susanne Hoche, Stefan Wrobel, Relational Learning Using Constrained Confidence-Rated Boosting. Proceedings of the Eleventh International Conference on Inductive Logic Programming, LNAI 2157. ISBN 3-540-42538-1, pp. 51–64. September 2001. PDF, 161 Kbytes.


In propositional learning, boosting has been a very popular technique for increasing the accuracy of classification learners. In firstorder learning, on the other hand, surprisingly little attention has been paid to boosting, perhaps due to the fact that simple forms of boosting lead to loss of comprehensibility and are too slow when used with standard ILP learners. In this paper, we show how both concerns can be addressed by using a recently proposed technique of constrained confidencerated boosting and a fast weak ILP learner.We give a detailed description of our algorithm and show on two standard benchmark problems that indeed such a weak learner can be boosted to perform comparably to state-of-the-art ILP systems while maintaining acceptable comprehensibility and obtaining short run-times.

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