Performance Evaluation of Feature Selection Algorithms on Human Activity Classification - Tulum, Gokalp; Artug, N. Tugrul; Bolat, Bulent (IEEE, 2013)
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.
Keywords: Feature selection; ReliefF; t-score; human activity detection; Computer Science; Engineering