Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2016 (2016), Article ID 9569161, 6 pages
http://dx.doi.org/10.1155/2016/9569161
Research Article

Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

Computer Engineering Department, Dumlupinar University, 43000 Kütahya, Turkey

Received 6 November 2015; Accepted 1 February 2016

Academic Editor: Thunshun W. Liao

Copyright © 2016 Gürcan Yavuz and Doğan Aydin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms,” Applied Soft Computing, vol. 18, pp. 261–276, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Tabakhi, P. Moradi, and F. Akhlaghian, “An unsupervised feature selection algorithm based on ant colony optimization,” Engineering Applications of Artificial Intelligence, vol. 32, pp. 112–123, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  4. S. Palanisamy and S. Kanmani, “Artificial Bee colony approach for optimizing feature selection,” International Journal of Computer Science, vol. 9, no. 3, pp. 432–438, 2012. View at Google Scholar
  5. M. Schiezaro and H. Pedrini, “Data feature selection based on Artificial Bee Colony algorithm,” EURASIP Journal on Image and Video Processing, vol. 2013, no. 1, article 47, 2013. View at Publisher · View at Google Scholar
  6. M. Y. Syarifahadilah, R. Abdullah, and I. Venkat, “ABC algorithm as feature selection for biomarker discovery in mass spectrometry analysis,” in Proceedings of the 4th Conference on Data Mining and Optimization (DMO '12), pp. 67–72, IEEE, Langkawi, Malaysia, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. M. S. Uzer, N. Yilmaz, and O. Inan, “Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification,” The Scientific World Journal, vol. 2013, Article ID 419187, 10 pages, 2013. View at Publisher · View at Google Scholar
  8. M. Akila, V. Suresh Kumar, N. Anusheela, and K. Sugumar, “A novel feature subset selection algorithm using artificial bee colony in keystroke dynamics,” in Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20–22, 2011: Volume 2, vol. 131 of Advances in Intelligent and Soft Computing, pp. 813–820, Springer, New Delhi, India, 2011. View at Publisher · View at Google Scholar
  9. D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. tr06, Erciyes University, Kayseri, Turkey, 2005. View at Google Scholar
  10. B. Akay and D. Karaboga, “A modified artificial bee colony algorithm for real-parameter optimization,” Information Sciences, vol. 192, pp. 120–142, 2012. View at Publisher · View at Google Scholar
  11. K. Diwold, A. Aderhold, A. Scheidler, and M. Middendorf, “Performance evaluation of artificial bee colony optimization and new selection schemes,” Memetic Computing, vol. 3, no. 3, pp. 149–162, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Zhu and S. Kwong, “Gbest-guided artificial bee colony algorithm for numerical function optimization,” Applied Mathematics and Computation, vol. 217, no. 7, pp. 3166–3173, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  13. B. Alatas, “Chaotic bee colony algorithms for global numerical optimization,” Expert Systems with Applications, vol. 37, no. 8, pp. 5682–5687, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. W. Feng Gao, S. Yang Liu, and L. Ling Huang, “Enhancing artificial bee colony algorithm using more information-based search equations,” Information Sciences, vol. 270, pp. 112–133, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. G. Pampara, N. Franken, and A. P. Engelbrecht, “Combining particle swarm optimisation with angle modulation to solve binary problems,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05), vol. 1, pp. 89–96, IEEE, Edinburgh, Scotland, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik, “Feature selection for SVMs,” in Proceeding of the Annual Conference on Neural Information Processing Systems (NIPS '00), vol. 12, pp. 668–674, Denver, Colo, USA, 2000.
  17. T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 2006. View at Publisher · View at Google Scholar
  18. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10–18, 2009. View at Publisher · View at Google Scholar
  19. K. Bache and M. Lichman, “UCI machine learning repository,” 2013, http://archive.ics.uci.edu/ml.