Relational Learning Using Constrained Confidence-Rated Boosting

Hoche, Susanne and Wrobel, Stefan (2001) Relational Learning Using Constrained Confidence-Rated Boosting. In: Inductive Logic Programming. Lecture Notes in Computer Science, 2157 . Springer Berlin / Heidelberg, FRA, pp. 51-64. ISBN 978-3-540-42538-0

Full text not available from this repository.

Abstract

In propositional learning, boosting has been a very popular technique for increasing the accuracy of classification learners. In first-order 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.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
Depositing User:	Vishal Gautam
Date Deposited:	18 Aug 2011 12:07
Last Modified:	15 Dec 2022 00:53
URI:	https://ueaeprints.uea.ac.uk/id/eprint/21940
DOI:	10.1007/3-540-44797-0_5

Actions (login required)

View Item