Performance Evaluation of Feature Selection Algorithms on Human Activity Classification

dc.contributor.author	Tulum, Gokalp
dc.contributor.author	Artug, N. Tugrul
dc.contributor.author	Bolat, Bulent
dc.date.accessioned	2021-12-21T08:46:43Z
dc.date.available	2021-12-21T08:46:43Z
dc.date.issued	2013
dc.identifier.isbn	978-1-4799-0661-1; 978-1-4799-0659-8
dc.identifier.uri	http://dspace.yeniyuzyil.edu.tr:8080/xmlui/handle/20.500.12629/2080
dc.description.abstract	In 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
dc.language.iso	English
dc.publisher	Ieee
dc.relation.ispartof	IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)
dc.title	Performance Evaluation of Feature Selection Algorithms on Human Activity Classification
dc.type	Proceedings Paper
dc.relation.journal	2013 Ieee Internatıonal Symposıum On Innovatıons In Intellıgent Systems And Applıcatıons (Ieee Inısta)

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