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dc.contributor.authorWare, Malcolm
dc.contributor.authorFrank, Eibe
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorHall, Mark A.
dc.contributor.authorWitten, Ian H.
dc.date.accessioned2008-10-13T03:18:16Z
dc.date.available2008-10-13T03:18:16Z
dc.date.issued2000-03
dc.identifier.citationWare, M., Frank, E., Holmes, G., Hall, M. & Witten, I.H. (2000). Interactive machine learning–letting users build classifiers. (Working paper 00/04). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1020
dc.description.abstractAccording to standard procedure, building a classifier is a fully automated process that follows data preparation by a domain expert. In contrast, interactive machine learning engages users in actually generating the classifier themselves. This offers a natural way of integrating background knowledge into the modeling stage–so long as interactive tools can be designed that support efficient and effective communication. This paper shows that appropriate techniques can empower users to create models that compete with classifiers built by state-of-the-art learning algorithms. It demonstrates that users–even users who are not domain experts–can often construct good classifiers, without any help from a learning algorithm, using a simple two-dimensional visual interface. Experiments demonstrate that, not surprisingly, success hinges on the domain: if a few attributes can support good predictions, users generate accurate classifiers, whereas domains with many high-order attribute interactions favor standard machine learning techniques. The future challenge is to achieve a symbiosis between human user and machine learning algorithm.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato, Department of Computer Scienceen_US
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectMachine learning
dc.titleInteractive machine learning–letting users build classifiersen_US
dc.typeWorking Paperen_US
uow.relation.series00/04
pubs.elements-id55117
pubs.place-of-publicationHamiltonen_NZ


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