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dc.contributor.authorPurvis, Martinen_NZ
dc.contributor.authorDeng, Jeremiah D.en_NZ
dc.contributor.authorPurvis, Maryam A.en_NZ
dc.date.available2011-04-07T03:05:51Z
dc.date.copyright2009-06en_NZ
dc.identifier.citationPurvis, M., Deng, J. D., & Purvis, M. A. (2009). Software effort estimation: Harmonizing algorithms and domain knowledge in an integrated data mining approach (Information Science Discussion Papers Series No. 2009/05). University of Otago. Retrieved from http://hdl.handle.net/10523/971en
dc.identifier.urihttp://hdl.handle.net/10523/971
dc.description.abstractSoftware development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often can not achieve satisfactory results. In this paper, we present an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the effectiveness of the proposed approach.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjectsoftware effort estimationen_NZ
dc.subjectmachine learningen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleSoftware effort estimation: Harmonizing algorithms and domain knowledge in an integrated data mining approachen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages15en_NZ
otago.date.accession2009-06-09 03:23:42en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints825en_NZ
otago.school.eprintsKnowledge, Intelligence & Web Informatics Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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otago.relation.number2009/05en_NZ
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