Comparative Analysis of Ensemble Learning Methods for Signal Classification

Yıldırım P., Birant K. U., Radevski V., Kut R. A., Birant D.

26th IEEE Signal Processing and Communications Applications Conference (SIU), İzmir, Türkiye, 2 - 05 Mayıs 2018 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2018.8404601
  • Basıldığı Şehir: İzmir
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: signal classification, ensemble learning, machine learning, NEURAL-NETWORK, ALGORITHM
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

In recent years, the machine learning algorithms commenced to be used widely in signal classification area as well as many other areas. Ensemble learning has become one of the most popular Machine Learning approaches due to the high classification performance it provides. In this study, the application of four fundamental ensemble learning methods (Bagging, Boosting, Stacking, and Voting) with five different classification algorithms (Neural Network, Support Vector Machines, k-Nearest Neighbor, Naive Bayes, and C4.5) with the most optimal parameter values on signal datasets is presented. In the experimental studies, ensemble learning methods were applied on 14 different signal datasets and the results were compared in terms of classification accuracy rates. According to the results, the best classification performance was obtained with the Random Forest algorithm which is a Bagging based method.