EVALUATING CLASSIFIER COMBINATION USING SIMULATED CLASSIFIERS

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dc.contributor.authorParker, Jameseng
dc.date.accessioned2008-05-08T18:39:52Z
dc.date.available2008-05-08T18:39:52Z
dc.date.computerscience2000-10-25eng
dc.date.issued2000-10-25eng
dc.description.abstractThe use of standard data sets with known properties is standard practice for the evaluation of composite classifiers. However, because the properties of the original test data cannot be specified in advance, it is difficult to conduct controlled tests on classifier combination methods. In these cases it is common to use simulated data, but a standard means for doing this has not evolved. Confusion matrices can also be used to create simulated classifiers having similar properties to the original and, by generating specific confusion matrices and basing a set of simulated classifiers on these, test suites can be designed that have pre-determined properties. This simulation allows controlled testing of classifier combination techniques. Such a scheme is described and evaluated using known combination methods and standard data sets, both to confirm the simulation and to demonstrate its flexibility.eng
dc.description.notesWe are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.caeng
dc.identifier.department2000-659-11eng
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/30938
dc.identifier.urihttp://hdl.handle.net/1880/46403
dc.language.isoEngeng
dc.publisher.corporateUniversity of Calgaryeng
dc.publisher.facultyScienceeng
dc.subjectComputer Scienceeng
dc.titleEVALUATING CLASSIFIER COMBINATION USING SIMULATED CLASSIFIERSeng
dc.typeunknown
thesis.degree.disciplineComputer Scienceeng
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