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dc.contributor.authorDriessens, Kurt
dc.contributor.authorReutemann, Peter
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorLeschi, Claire
dc.coverage.spatialConference held at Singaporeen_NZ
dc.identifier.citationDriessens, K., Reutemann, P., Pfahringer, B. & Leschi, C.(2006). Using weighted nearest neighbor to benefit from unlabeled data. In W.K. Ng, M. Kitsuregawa & J. Li(Eds.), Proceedings of 10th Pacific-Asia Conference, PAKDD, Singapore, April 9-12,2006(pp. 97-106). Berlin: Springer.en_US
dc.description.abstractThe development of data-mining applications such as textclassification and molecular profiling has shown the need for machine learning algorithms that can benefit from both labeled and unlabeled data, where often the unlabeled examples greatly outnumber the labeled examples. In this paper we present a two-stage classifier that improves its predictive accuracy by making use of the available unlabeled data. It uses a weighted nearest neighbor classification algorithm using the combined example-sets as a knowledge base. The examples from the unlabeled set are “pre-labeled” by an initial classifier that is build using the limited available training data. By choosing appropriate weights for this pre-labeled data, the nearest neighbor classifier consistently improves on the original classifier.en_US
dc.publisherSpringer, Berlinen_US
dc.sourcePAKDD 2006en_NZ
dc.subjectcomputer scienceen_US
dc.subjectdata miningen_US
dc.subjectnearest neighbour classifieren_US
dc.subjectunlabeled dataen_US
dc.subjectMachine learning
dc.titleUsing weighted nearest neighbor to benefit from unlabeled dataen_US
dc.typeConference Contributionen_US
dc.relation.isPartOfProc 10th Pacific-Asia Conference: Advances in Knowledge Discovery and Data Miningen_NZ
pubs.volumeLNCS 3918en_NZ

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