Performance evaluation of feature selection algorithms on human activity classification

dc.contributor.author	Tulum, G.
dc.contributor.author	Artug, N.T.
dc.contributor.author	Bolat, B.
dc.date.accessioned	2021-12-21T08:41:50Z
dc.date.available	2021-12-21T08:41:50Z
dc.date.issued	2013
dc.identifier.isbn	9.78148E+12
dc.identifier.uri	https://doi.org/10.1109/INISTA.2013.6577634
dc.identifier.uri	http://dspace.yeniyuzyil.edu.tr:8080/xmlui/handle/20.500.12629/1529
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.title	Performance evaluation of feature selection algorithms on human activity classification
dc.type	Conference Paper
dc.relation.journal	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
dc.identifier.doi	10.1109/INISTA.2013.6577634