Efficient Generation and Selection of Combined Features for Improved Classification

Handle URI:
http://hdl.handle.net/10754/316714
Title:
Efficient Generation and Selection of Combined Features for Improved Classification
Authors:
Shono, Ahmad N.
Abstract:
This 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.
Advisors:
Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Committee Member:
Gao, Xin ( 0000-0002-7108-3574 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
May-2014
Type:
Thesis
Appears in Collections:
Theses; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorBajic, Vladimir B.en
dc.contributor.authorShono, Ahmad N.en
dc.date.accessioned2014-05-11T07:14:28Z-
dc.date.available2014-05-11T07:14:28Z-
dc.date.issued2014-05en
dc.identifier.urihttp://hdl.handle.net/10754/316714en
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.en
dc.language.isoenen
dc.subjectClassificationen
dc.subjectCombineden
dc.subjectDiscoveryen
dc.subjectDCFDen
dc.subjectFeaturesen
dc.titleEfficient Generation and Selection of Combined Features for Improved Classificationen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberGao, Xinen
dc.contributor.committeememberMoshkov, Mikhailen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id124292en
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