Opposition based Gray Wolf Algorithm for Feature Selection in Classification Problems Siniflandirma Problemlerinde Özellik Secimi icin Karsitlik Tabanli Gri Kurt Algoritmasi

Karakas M., YÜZGEÇ U.

3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019, Ankara, Türkiye, 11 - 13 Ekim 2019 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/ismsit.2019.8932757
  • Basıldığı Şehir: Ankara
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Classification, Feature selection, Gray Wolf Algorithm, Opposition based learning, Optimization
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

© 2019 IEEE.With the rapid development of technology, the data is classified by distributing among various classes defined on a dataset for easier and faster access to increased data. By using classification algorithms developed for these classification processes, the data is clustered according to similar criteria. These classification algorithms are taught by training with the given training set, and then try to classify the data correctly when the test data that is not given the class is sent. Heuristic algorithms have become very popular in optimization problems in recent years. Gray Wolf Optimization (GWO) algorithm is a meta-heuristic optimization algorithm inspired by the life and hunting strategies of gray wolves in nature. In this study, Gray Wolf Algorithm was developed by using opposition based learning method for different feature selection of certain classifiers (such as KNN & SVM). The proposed improved GWO algorithm was compared with the original GWO algorithm for classification test data sets obtained from the literature.