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dc.contributor.authorGreen, P. D.
dc.contributor.authorLane, P.C.R.
dc.contributor.authorRainer, A.
dc.contributor.authorScholz, S.
dc.date.accessioned2013-01-15T12:59:05Z
dc.date.available2013-01-15T12:59:05Z
dc.date.issued2010
dc.identifier.citationGreen , P D , Lane , P C R , Rainer , A & Scholz , S 2010 , Selecting Features in Origin Analysis . in Research and Development in Intelligent Systems XXVII, Incorporating Applications and Innovations in Intelligent Systems XVIII, : Proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence . Springer , pp. 379-392 .
dc.identifier.isbn978-0-85729-129-5
dc.identifier.isbn978-0-85729-130-1
dc.identifier.otherPURE: 98385
dc.identifier.otherPURE UUID: 536282e4-98a6-432d-a4a3-0e196198c1e4
dc.identifier.otherdspace: 2299/4913
dc.identifier.urihttp://hdl.handle.net/2299/9655
dc.descriptionOriginal paper can be found at: http://www.springer.com/computer/ai/book/978-0-85729-129-5 Copyright Springer
dc.description.abstractWhen applying a machine-learning approach to develop classifiers in a new domain, an important question is what measurements to take and how they will be used to construct informative features. This paper develops a novel set of machine-learning classifiers for the domain of classifying files taken from software projects; the target classifications are based on origin analysis. Our approach adapts the output of four copy-analysis tools, generating a number of different measurements. By combining the measures and the files on which they operate, a large set of features is generated in a semi-automatic manner. After which, standard attribute selection and classifier training techniques yield a pool of high quality classifiers (accuracy in the range of 90%), and information on the most relevant features.en
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofResearch and Development in Intelligent Systems XXVII, Incorporating Applications and Innovations in Intelligent Systems XVIII,
dc.subjectdata mining
dc.subjectfeature construction
dc.subjectorigin analysis
dc.subjectmachine learning
dc.titleSelecting Features in Origin Analysisen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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