Classifier-based constraint acquisition

Simple item page
dc.contributor.authorPrestwich, Steven D.
dc.contributor.authorFreuder, Eugene C.
dc.contributor.authorO'Sullivan, Barry
dc.contributor.authorBrowne, David
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2021-04-29T15:17:45Z
dc.date.available2021-04-29T15:17:45Z
dc.date.issued2021-04-17
dc.date.updated2021-04-29T15:03:20Z
dc.description.abstractModeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions and (usually) non-solutions. Active methods query an oracle while passive methods do not. We propose a known but not widely-used application of machine learning to constraint acquisition: training a classifier to discriminate between solutions and non-solutions, then deriving a constraint model from the trained classifier. We discuss a wide range of possible new acquisition methods with useful properties inherited from classifiers. We also show the potential of this approach using a Naive Bayes classifier, obtaining a new passive acquisition algorithm that is considerably faster than existing methods, scalable to large constraint sets, and robust under errors.en
dc.description.sponsorshipScience Foundation Ireland (under Grant No. 12/RC/2289-P2 which is co-funded under the European Regional Development Fund)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationPrestwich, S. D., Freuder, E. C., O’Sullivan, B. and Browne, D. (2021) 'Classifier-based constraint acquisition', Annals of Mathematics and Artificial Intelligence, (20 pp). doi: 10.1007/s10472-021-09736-4en
dc.identifier.doi10.1007/s10472-021-09736-4en
dc.identifier.endpage20en
dc.identifier.issn1573-7470
dc.identifier.journaltitleAnnals of Mathematics and Artificial Intelligenceen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/11237
dc.language.isoenen
dc.publisherSpringeren
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://link.springer.com/article/10.1007/s10472-021-09736-4
dc.rights© The Author(s) 2021.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectConstraint acquisitionen
dc.subjectClassifieren
dc.subjectBayesianen
dc.subjectBoolean satisfiabilityen
dc.titleClassifier-based constraint acquisitionen
dc.typeArticle (peer-reviewed)en
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Prestwich2021_Article_Classifier-basedConstraintAcqu.pdf
Size:
554.14 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Computer Science - Journal Articles
Insight Centre for Data Analytics - Journal Articles