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dc.contributor.authorTsymbal, Alexey
dc.date.accessioned2008-01-29T10:27:06Z
dc.date.available2008-01-29T10:27:06Z
dc.date.issued2006en
dc.identifier.citationTsymbal, Alexey. 'Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2006-25, 2006, pp6en
dc.identifier.otherTCD-CS-2006-25
dc.identifier.urihttp://hdl.handle.net/2262/13498
dc.description.abstractInductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning system can achieve depend on the quality of data and on the appropriate selection of a learning algorithm for the data. In this paper we analyze the effect of class noise on supervised learning in medical domains. We review the related work on learning from noisy data and propose to use feature extraction as a pre-processing step to diminish the effect of class noise on the learning process. Our experiments with 8 medical datasets show that feature extraction indeed helps to deal with class noise. It clearly results in higher classification accuracy of learnt models without the separate explicit elimination of noisy instances.en
dc.format.extent119624 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherTrinity College Dublin, Department of Computer Scienceen
dc.relation.ispartofseriesComputer Science Technical Reporten
dc.relation.ispartofseriesTCD-CS-2006-25en
dc.relation.haspartTCD-CS-[no.]en
dc.subjectComputer Scienceen
dc.titleClass Noise and Supervised Learning in Medical Domains: The Effect of Feature Extractionen
dc.typeTechnical Reporten
dc.contributor.sponsorScience Foundation Ireland
dc.identifier.rssurihttps://www.cs.tcd.ie/publications/tech-reports/reports.06/TCD-CS-2006-25.pdf


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