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dc.contributor.authorCunningham, Padraig
dc.contributor.authorCarney, John G.
dc.date.accessioned2008-01-07T11:47:01Z
dc.date.available2008-01-07T11:47:01Z
dc.date.issued2000-01
dc.identifier.citationCunningham, Padraig; Carney, John G. 'Diversity versus Quality in Classification Ensembles based on Feature Selection'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2000-02, 2000, pp9en
dc.identifier.otherTCD-CS-2000-02
dc.identifier.urihttp://hdl.handle.net/2262/13079
dc.description.abstractFeature subset-selection has emerged as a useful technique for creating diversity in ensembles ? particularly in classification ensembles. In this paper we argue that this diversity needs to be monitored in the creation of the ensemble. We propose an entropy measure of the outputs of the ensemble members as a useful measure of the ensemble diversity. Further, we show that using the associated conditional entropy as a loss function (error measure) works well and the entropy in the ensemble predicts well the reduction in error due to the ensemble. These measures are evaluated on a medical prediction problem and are shown to predict the performance of the ensemble well. We also show that the entropy measure of diversity has the added advantage that it seems to model the change in diversity with the size of the ensemble.en
dc.format.extent37774 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-2000-02en
dc.relation.haspartTCD-CS-[no.]en
dc.subjectComputer Scienceen
dc.titleDiversity versus Quality in Classification Ensembles based on Feature Selectionen
dc.typeTechnical Reporten
dc.identifier.rssurihttps://www.cs.tcd.ie/publications/tech-reports/reports.00/TCD-CS-2000-02.pdf


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