Publication:
Are we losing accuracy while gaining confidence in induced rules – an assessment of PrIL

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dc.conference.dateAUGUST 20-21, 1995
dc.conference.locationMontreal
dc.contributor.coauthorWallace, W
dc.contributor.departmentDepartment of Business Administration
dc.contributor.kuauthorFaculty Member, Ali, Özden Gür
dc.contributor.schoolcollegeinstituteCollege of Administrative Sciences and Economics
dc.date.accessioned2025-10-30T11:13:23Z
dc.date.available2025-10-30
dc.date.issued1995
dc.description.abstractProbabilistic Inductive Learning (PrIL), is a tree induction algorithm that provides a minimum correct classification level with a specified confidence for each rule in the decision tree, This feature is particularly useful in uncertain environments where decisions are based on the induced rules. This paper provides a concise description of (the extended) PrIL and demonstrates that its performance is as good as best results in the machine learning literature, using datasets from the data repository at UC Irvine.
dc.description.fulltextYes
dc.description.harvestedfromManual
dc.description.indexedbyScopus
dc.description.openaccessEditöryel Kontrolde bakılacak (Bu alan ilgili koleksiyona geçirilirken boşaltılıp öyle atılacak drop-down menü sonrasında ilgili koleksiyonda gelerek doğru alan seçilecek.)
dc.description.peerreviewstatusPeer-Reviewed
dc.description.publisherscopeInternational
dc.description.readpublishN/A
dc.description.versionPublished Version
dc.identifier.embargoNo
dc.identifier.endpage14
dc.identifier.isbn9780929280820
dc.identifier.isbn0929280822
dc.identifier.linkhttps://cdn.aaai.org/KDD/1995/KDD95-002.pdf
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-26544464568
dc.identifier.startpage9
dc.identifier.urihttps://hdl.handle.net/20.500.14288/31297
dc.keywordsClassification
dc.keywordsRule reliability
dc.language.isoeng
dc.publisherAAAI Press
dc.relation.affiliationKoç University
dc.relation.collectionKoç University Institutional Repository
dc.relation.ispartofKDD 1995 - Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining
dc.relation.openaccessYes
dc.rightsEditöryel Kontrolde bakılacak (Bu alan ilgili koleksiyona geçirilirken boşaltılıp öyle atılacak drop-down menü sonrasında ilgili koleksiyonda gelerek doğru alan seçilecek.)
dc.rights.uriEditöryel Kontrolde bakılacak (Bu alan ilgili koleksiyona geçirilirken boşaltılıp öyle atılacak drop-down menü sonrasında ilgili koleksiyonda gelerek doğru alan seçilecek.)
dc.subjectMachine learning
dc.titleAre we losing accuracy while gaining confidence in induced rules – an assessment of PrIL
dc.typeConference Proceeding
dspace.entity.typePublication
relation.isOrgUnitOfPublicationca286af4-45fd-463c-a264-5b47d5caf520
relation.isOrgUnitOfPublication.latestForDiscoveryca286af4-45fd-463c-a264-5b47d5caf520
relation.isParentOrgUnitOfPublication972aa199-81e2-499f-908e-6fa3deca434a
relation.isParentOrgUnitOfPublication.latestForDiscovery972aa199-81e2-499f-908e-6fa3deca434a

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