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dc.contributor.authorTsymbal, Alexey
dc.date.accessioned2007-12-12T11:16:05Z
dc.date.available2007-12-12T11:16:05Z
dc.date.issued2003en
dc.identifier.citationTsymbal, Alexey. 'Feature Extraction for Classification in Knowledge'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2003-32, 2003, pp7en
dc.identifier.otherTCD-CS-2003-32
dc.identifier.urihttp://hdl.handle.net/2262/12581
dc.description.abstractDimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of ?the curse of dimensionality?. We consider three different eigenvector-based feature extraction approaches for classification. The summary of obtained results concerning the accuracy of classification schemes is presented and the issue of search for the most appropriate feature extraction method for a given data set is considered. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.en
dc.format.extent157310 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-2003-32en
dc.relation.haspartTCD-CS-[no.]en
dc.subjectComputer Scienceen
dc.titleFeature Extraction for Classification in Knowledgeen
dc.typeTechnical Reporten
dc.contributor.sponsorScience Foundation Ireland
dc.identifier.rssurihttps://www.cs.tcd.ie/publications/tech-reports/reports.03/TCD-CS-2003-32.pdf


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