Performance Evaluation of Feature Selection Algorithms on Human Activity Classification


Tulum G., Artug N. T. , BOLAT B.

IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), Bulgaristan, 19 - 21 Haziran 2013 identifier identifier

Özet

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 result is obtained as 97.6% with 51 features selected by ReliefF.