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dc.contributor.authorTsymbal, Alexey
dc.contributor.authorCunningham, Padraig
dc.date.accessioned2007-12-11T12:10:07Z
dc.date.available2007-12-11T12:10:07Z
dc.date.issued2003en
dc.identifier.citationTsymbal, Alexey; Cunningham, Padraig. 'Search Strategies for Ensemble Feature Selection in Medical Diagnostics'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2003-22, 2003, pp6en
dc.identifier.otherTCD-CS-2003-22
dc.identifier.urihttp://hdl.handle.net/2262/12560
dc.description.abstractThe goal of this paper is to propose, evaluate, and compare four search strategies for ensemble feature selection, and to consider their application to medical diagnostics, with a focus on the problem of the classification of acute abdominal pain. Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to get higher accuracy, sensitivity, and specificity, which are often not achievable with single models. One technique, which proved to be effective for ensemble construction, is feature selection. Lately, several strategies for ensemble feature selection were proposed, including random subspacing, hill-climbing-based search, and genetic search. In this paper, we propose two new sequential-search-based strategies for ensemble feature selection, and evaluate them, constructing ensembles of simple Bayesian classifiers for the problem of acute abdominal pain classification. We compare the search strategies with regard to achieved accuracy, sensitivity, specificity, and the average number of features they select.en
dc.format.extent52872 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-2003-22en
dc.relation.haspartTCD-CS-[no.]en
dc.subjectComputer Scienceen
dc.titleSearch Strategies for Ensemble Feature Selection in Medical Diagnosticsen
dc.typeTechnical Reporten
dc.contributor.sponsorScience Foundation Ireland
dc.identifier.rssurihttps://www.cs.tcd.ie/publications/tech-reports/reports.03/TCD-CS-2003-22.pdf


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