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dc.contributor.advisorBajic, Vladimir B.
dc.contributor.authorShono, Ahmad N.
dc.date.accessioned2014-05-11T07:14:28Z
dc.date.available2014-05-11T07:14:28Z
dc.date.issued2014-05
dc.identifier.doi10.25781/KAUST-6H6ST
dc.identifier.urihttp://hdl.handle.net/10754/316714
dc.description.abstractThis study contributes a methodology and associated toolkit developed to allow users to experiment with the use of combined features in classification problems. Methods are provided for efficiently generating combined features from an original feature set, for efficiently selecting the most discriminating of these generated combined features, and for efficiently performing a preliminary comparison of the classification results when using the original features exclusively against the results when using the selected combined features. The potential benefit of considering combined features in classification problems is demonstrated by applying the developed methodology and toolkit to three sample data sets where the discovery of combined features containing new discriminating information led to improved classification results.
dc.language.isoen
dc.subjectClassification
dc.subjectCombined
dc.subjectDiscovery
dc.subjectDCFD
dc.subjectFeatures
dc.titleEfficient Generation and Selection of Combined Features for Improved Classification
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberGao, Xin
dc.contributor.committeememberMoshkov, Mikhail
thesis.degree.disciplineComputer Science
thesis.degree.nameMaster of Science
refterms.dateFOA2018-06-13T17:51:41Z


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