Performance evaluation of feature selection algorithms on human activity classification

TULUM G., ARTUĞ N. T., Bölat B.

2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013, Albena, Bulgaristan, 19 - 21 Haziran 2013, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/inista.2013.6577634
  • Basıldığı Şehir: Albena
  • Basıldığı Ülke: Bulgaristan
  • Anahtar Kelimeler: Feature selection, human activity detection, ReliefF, t-score
  • İstanbul Yeni Yüzyıl Üniversitesi Adresli: Evet

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. © 2013 IEEE.