Relational Learning Using Constrained Confidence-Rated Boosting

S Hoche, S Wrobel

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

10 Citations (Scopus)

Abstract

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.
Translated title of the contributionRelational Learning Using Constrained Confidence-Rated Boosting
Original languageEnglish
Title of host publicationUnknown
PublisherSpringer
Pages51 - 64
Number of pages13
ISBN (Print)3540425381
Publication statusPublished - Sep 2001

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the Eleventh International Conference on Inductive Logic Programming, LNAI 2157

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