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dc.contributor.authorTsymbal, Alexey
dc.contributor.authorCunningham, Padraig
dc.date.accessioned2008-01-24T11:05:55Z
dc.date.available2008-01-24T11:05:55Z
dc.date.issued2005en
dc.identifier.citationTsymbal, Alexey; Cunningham, Padraig. 'Sequential Genetic Search for Ensemble Feature Selection'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2005-40, 2005, pp6en
dc.identifier.otherTCD-CS-2005-40
dc.identifier.urihttp://hdl.handle.net/2262/13358
dc.description.abstractEnsemble learning constitutes one of the main directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. One technique, which proved to be effective for constructing an ensemble of diverse classifiers, is the use of feature subsets. Among different approaches to ensemble feature selection, genetic search was shown to perform best in many domains. In this paper, a new strategy GAS-SEFS, Genetic Algorithm-based Sequential Search for Ensemble Feature Selection, is introduced. Instead of one genetic process, it employs a series of processes, the goal of each of which is to build one base classifier. Experiments on 21 data sets are conducted, comparing the new strategy with a previously considered genetic strategy for different ensemble sizes and for five different ensemble integration methods. The experiments show that GAS-SEFS, although being more time-consuming, often builds better ensembles, especially on data sets with larger numbers of features.en
dc.format.extent259094 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-2005-40en
dc.relation.haspartTCD-CS-[no.]en
dc.subjectComputer Scienceen
dc.titleSequential Genetic Search for Ensemble Feature Selectionen
dc.typeTechnical Reporten
dc.contributor.sponsorScience Foundation Ireland
dc.identifier.rssurihttps://www.cs.tcd.ie/publications/tech-reports/reports.05/TCD-CS-2005-40.pdf


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