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Performance Evaluation of Feature Selection Algorithms on Human Activity Classification

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dc.contributor.authorTulum, Gokalp
dc.contributor.authorArtug, N. Tugrul
dc.contributor.authorBolat, Bulent
dc.date.accessioned2026-06-27T13:30:40Z
dc.date.issued2013
dc.description.abstractIn this work, four human activities were classified by using multi layer perceptron and k-nearest neighbours algorithm. Due to mass amount of data, two different feature selection methods, which are ReliefF and t-score, were applied to the data. The best result is obtained as 97.6% with 51 features selected by ReliefF.en
dc.identifier.isbn978-1-4799-0661-1; 978-1-4799-0659-8
dc.identifier.urihttps://hdl.handle.net/20.500.14981/53381
dc.identifier.wos000332186500020
dc.language.isoeng
dc.publisherIEEE
dc.relation.conferenceIEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)
dc.relation.ispartof2013 IEEE INTERNATIONAL SYMPOSIUM ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (IEEE INISTA)
dc.subjectFeature selection
dc.subjectReliefF
dc.subjectt-score
dc.subjecthuman activity detection
dc.subjectComputer Science
dc.subjectEngineering
dc.titlePerformance Evaluation of Feature Selection Algorithms on Human Activity Classification
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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