BibTex RIS Kaynak Göster

Nitelik Seçimi için Yapay Arı Kolonisi Tabanlı Bir Algoritma

Yıl 2015, Cilt: 30 Sayı: 1, 25 - 32, 25.07.2016
https://doi.org/10.21605/cukurovaummfd.242789

Öz

The aim of the feature selection is to reduce the number of features to be used during the classification process to improve run-time performance and efficiency of the classifier. In this study, Artificial Bee Colony (ABC) Optimization Technique, which is a recent successful swarm intelligence algorithm, based feature selection method is proposed for classification tasks. The algorithm was experimented on fifteen datasets from the UCI Repository which are commonly used in classification problems. The experimental results of this study showed that the proposed ABC based algorithm is able to select good features for classification tasks

Kaynakça

  • 1. He, X., Zhang, Q., Sun, N., Dong, Y. 2009. Feature Selection with Discrete Binary Differential Evolution. In Artificial Intelligence and Computational Intelligence, AICI'09 International Conference, 4, p. 327 330.
  • 2. Frohlich, H., Chapelle, O., Scholkopf, B., 2003. Feature Selection for Support Vector Machines by means of Genetic Algorithm. In Tools with Artificial Intelligence, 15th IEEE International Conference, p. 142-148.
  • 3. Karaboğa, D., 2005. An Idea based on Honey Bee Swarm for Numerical Optimization. Erciyes University, Engineering Faculty, Computer Engineering Department, Technical report.
  • 4. Bolaji, A. L. A., Khader, A. T., Al-Betar, M. A., Awadallah, M. A. 2013. Artificial Bee Colony Algorithm, Its Variants and Applications: A Survey. Journal of Theoretical and App
  • 5. Saeys, Y., Inza, I., Larrañaga, P., 2007. A Review of Feature Selection Techniques in Bioinformatics. Bioinformatics, 23(19), p. 2507-2517.
  • 6. Palanisamy, S., Kanmani, S., 2012. Artificial Bee Colony Approach for Optimizing Feature Selection. International Journal of Computer Science Issues, 9(3), p. 432-438.
  • 7. Prasartvit, T., Banharnsakun, A., Kaewkamnerdpong, B. and Achalakul, T., 2013. Reducing Bioinformatics Data Dimension with ABC-kNN. Neurocomputing, 116, p. 367-381.
  • 8. Schiezaro, M. and Pedrini, H., 2013. Data Feature Selection based on Artificial Bee Colony Algorithm. EURASIP Journal on Image and Video Processing, 1, p. 1-8.
  • 9. Uzer, M. S., Yilmaz, N., Inan, O., 2013. Feature Selection Method based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification. The Scientific World Journal, p. 1-10.
  • 10. Saraç, E., Özel, S. A., 2010. URL-Based Web Page Classification. ASYU Symposium, p. 13-17.

An Artificial Bee Colony Based Algorithm for Feature Selection

Yıl 2015, Cilt: 30 Sayı: 1, 25 - 32, 25.07.2016
https://doi.org/10.21605/cukurovaummfd.242789

Öz

Nitelik seçiminin amacı çalışma zamanını ve sınıflandırıcının verimliliğini iyileştirmek için sınıflandırma işlemi sırasında kullanılacak olan özellik sayısını azaltmaktır. Bu çalışmada, sınıflandırma işlemleri için yakın zamanda geliştirilmiş başarılı bir sürü zekası algoritması olan Yapay Arı Kolonisi (YAK) Optimizasyon Tekniğine dayalı bir nitelik seçim yöntemi geliştirilmiştir. Geliştirilen yöntem UCI veritabanından elde edilen ve sınıflandırma problemlerinde sıklıkla kullanılan 15 veri kümesi üzerinde sınanmıştır. Deney sonuçları önerilen YAK tabanlı algoritmanın sınıflandıma çalışmaları için iyi nitelikleri seçebildiğini göstermiştir

Kaynakça

  • 1. He, X., Zhang, Q., Sun, N., Dong, Y. 2009. Feature Selection with Discrete Binary Differential Evolution. In Artificial Intelligence and Computational Intelligence, AICI'09 International Conference, 4, p. 327 330.
  • 2. Frohlich, H., Chapelle, O., Scholkopf, B., 2003. Feature Selection for Support Vector Machines by means of Genetic Algorithm. In Tools with Artificial Intelligence, 15th IEEE International Conference, p. 142-148.
  • 3. Karaboğa, D., 2005. An Idea based on Honey Bee Swarm for Numerical Optimization. Erciyes University, Engineering Faculty, Computer Engineering Department, Technical report.
  • 4. Bolaji, A. L. A., Khader, A. T., Al-Betar, M. A., Awadallah, M. A. 2013. Artificial Bee Colony Algorithm, Its Variants and Applications: A Survey. Journal of Theoretical and App
  • 5. Saeys, Y., Inza, I., Larrañaga, P., 2007. A Review of Feature Selection Techniques in Bioinformatics. Bioinformatics, 23(19), p. 2507-2517.
  • 6. Palanisamy, S., Kanmani, S., 2012. Artificial Bee Colony Approach for Optimizing Feature Selection. International Journal of Computer Science Issues, 9(3), p. 432-438.
  • 7. Prasartvit, T., Banharnsakun, A., Kaewkamnerdpong, B. and Achalakul, T., 2013. Reducing Bioinformatics Data Dimension with ABC-kNN. Neurocomputing, 116, p. 367-381.
  • 8. Schiezaro, M. and Pedrini, H., 2013. Data Feature Selection based on Artificial Bee Colony Algorithm. EURASIP Journal on Image and Video Processing, 1, p. 1-8.
  • 9. Uzer, M. S., Yilmaz, N., Inan, O., 2013. Feature Selection Method based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification. The Scientific World Journal, p. 1-10.
  • 10. Saraç, E., Özel, S. A., 2010. URL-Based Web Page Classification. ASYU Symposium, p. 13-17.
Toplam 10 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA34CK76SM
Bölüm Makaleler
Yazarlar

Ezgi Zorarpacı Bu kişi benim

Selma Ayşe Özel Bu kişi benim

Süleyman Güngör Bu kişi benim

Yayımlanma Tarihi 25 Temmuz 2016
Yayımlandığı Sayı Yıl 2015 Cilt: 30 Sayı: 1

Kaynak Göster

APA Zorarpacı, E., Özel, S. A., & Güngör, S. (2016). An Artificial Bee Colony Based Algorithm for Feature Selection. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 30(1), 25-32. https://doi.org/10.21605/cukurovaummfd.242789
AMA Zorarpacı E, Özel SA, Güngör S. An Artificial Bee Colony Based Algorithm for Feature Selection. cukurovaummfd. Temmuz 2016;30(1):25-32. doi:10.21605/cukurovaummfd.242789
Chicago Zorarpacı, Ezgi, Selma Ayşe Özel, ve Süleyman Güngör. “An Artificial Bee Colony Based Algorithm for Feature Selection”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 30, sy. 1 (Temmuz 2016): 25-32. https://doi.org/10.21605/cukurovaummfd.242789.
EndNote Zorarpacı E, Özel SA, Güngör S (01 Temmuz 2016) An Artificial Bee Colony Based Algorithm for Feature Selection. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 30 1 25–32.
IEEE E. Zorarpacı, S. A. Özel, ve S. Güngör, “An Artificial Bee Colony Based Algorithm for Feature Selection”, cukurovaummfd, c. 30, sy. 1, ss. 25–32, 2016, doi: 10.21605/cukurovaummfd.242789.
ISNAD Zorarpacı, Ezgi vd. “An Artificial Bee Colony Based Algorithm for Feature Selection”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 30/1 (Temmuz 2016), 25-32. https://doi.org/10.21605/cukurovaummfd.242789.
JAMA Zorarpacı E, Özel SA, Güngör S. An Artificial Bee Colony Based Algorithm for Feature Selection. cukurovaummfd. 2016;30:25–32.
MLA Zorarpacı, Ezgi vd. “An Artificial Bee Colony Based Algorithm for Feature Selection”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, c. 30, sy. 1, 2016, ss. 25-32, doi:10.21605/cukurovaummfd.242789.
Vancouver Zorarpacı E, Özel SA, Güngör S. An Artificial Bee Colony Based Algorithm for Feature Selection. cukurovaummfd. 2016;30(1):25-32.