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A Class-Specific Ensemble Feature Selection Approach for Classification Problems


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dc.contributor.advisorGilbert, Juan
dc.contributor.authorSoares, Caio
dc.date.accessioned2009-04-20T20:58:21Z
dc.date.available2009-04-20T20:58:21Z
dc.date.issued2009-04-20T20:58:21Z
dc.identifier.urihttp://hdl.handle.net/10415/1659
dc.description.abstractThis research proposes a new feature selection algorithm, Class-specific Ensemble Feature Selection (CEFS), which finds class-specific subsets of features optimal to each available classification in the dataset. Each subset is then combined with a classifier to create an ensemble feature selection model which is further used to predict unseen instances. CEFS attempts to provide the diversity and base classifier disagreement sought after in effective ensemble models by providing highly useful, yet highly exclusive feature subsets. Also, the use of a wrapper method gives each subset the chance to perform optimally under the respective base classifier. Preliminary experiments implementing this innovative approach suggest potential improvements of more than 10% over existing methods.en
dc.rightsEMBARGO_NOT_AUBURNen
dc.subjectComputer Scienceen
dc.titleA Class-Specific Ensemble Feature Selection Approach for Classification Problemsen
dc.typethesisen
dc.embargo.lengthNO_RESTRICTIONen_US
dc.embargo.statusNOT_EMBARGOEDen_US

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