Relational Learning Using Constrained Confidence-Rated Boosting

Susanne Hoche, Stefan Wrobel

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationInductive Logic Programming
EditorsCéline Rouveirol, Michéle Sebag
PublisherSpringer Berlin / Heidelberg
Pages51-64
Number of pages14
Volume2157
ISBN (Print)978-3-540-42538-0
DOIs
Publication statusPublished - 2001
EventProceedings of the Eleventh International Conference, ILP - Strasbourg, France
Duration: 9 Sep 200111 Sep 2001

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg

Conference

ConferenceProceedings of the Eleventh International Conference, ILP
CountryFrance
CityStrasbourg
Period9/09/0111/09/01

Cite this