Open Access
MATEC Web of Conferences
Volume 150, 2018
Malaysia Technical Universities Conference on Engineering and Technology (MUCET 2017)
Article Number 06006
Number of page(s) 6
Section Information & Communication Technology (ICT), Science (SCI) & Mathematics (SM)
Published online 23 February 2018
  1. R. Amirreza, N. Hossein, A hybrid feature selection approach based on ensemble method for high-dimensional data, 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), 7-9 March, pp. 16-20, (2017) [Google Scholar]
  2. Y. Zhang, A. Yang, C. Xiong, T. Wang, Z. Zhang, “Feature selection suing data envelopment analysis”, Knowledge-Based Systems Journal, 64, pp. 70-80, April 2014, (2014) [CrossRef] [Google Scholar]
  3. Y. Sayes, I. Inza,, P. Larranaga, A review of feature selection techniques in bio-informatics, Bioinformatics vol. 23, no. 19, pp. 2507 – 2517, (2007) [Google Scholar]
  4. X.S. Yang, A new metaheuristic Bat-inspired algorithm, in: J.R. Gonzalez, et al.(Eds.), Nature Inspired Cooperative Strategies for Optimization (NISCO 2010). Studies in Computational Intelligence, Springer Berlin, Springer, Berlin, August 2010, pp. 65–74, (2010) [CrossRef] [Google Scholar]
  5. J. Han, and M. Kamber, Data Mining; Concepts and Techniques. Morgan Kaufmann Publishers, 2000. [Google Scholar]
  6. H. Almuallim, T. G. Dietterich, T. G. Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, vol. 69, no. 1 – 2, pp. 279–305, (1994) [CrossRef] [Google Scholar]
  7. D. Koller, M. Sahami, M. Toward optimal feature selection. In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 284–292, (1996) [Google Scholar]
  8. M. A. Hall, L. A. Smith, Feature Subset Selection: A Correlation Based Filter Approach, In 1997 International Conference on Neural Information Processing and Intelligent Information Systems, pp. 855–858, (1997) [Google Scholar]
  9. M. Y. Munirah, M. Rozlini, N. Wahid, A comparative analysis on feature selection techniques for medical datasets, APRN Journal of Engineering and Applied Sciences, vol 11, no 22, November 2016, (2016) [Google Scholar]
  10. C. Shang, M. Li, S. Feng, Q. Jiang, J. Fan, Feature Selection via Maximizing Global Information Gain for Text Classification, Knowledge-Based Systems, vol. 54, 298–309, (2013) [CrossRef] [Google Scholar]
  11. J. H. Lee, J. R. Anaraki, C. W. Ahn, J. An, Eficient Classification System based on Fuzzy-Rough Feature Selection and Multitree Genetic Programming for intension Pattern Recognition using Brain Signal, Expert Systems with Applications, vol. 42, 1644–1651, (2015) [CrossRef] [Google Scholar]
  12. S. Kashef, H. Nezamabadi-pour, An Advanced ACO Algorithm for Feature Subset Selection, Neurocomputing vol. 147, 271–279, (2015). [Google Scholar]
  13. Y. Zhang, D. Gong, Y. Hu, W. Zhang,. Feature Selection Algorithm based on Bare Bones Particle Swarm Optimazation, Neurocomputing, vol. 148, pp.150–157, (2015) [Google Scholar]
  14. Y. Shen-Lan, R. Gang, F. Yi-Ping, Multiple kernel learning based feature selection for process monitoring, 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 24 – 16 May 2017, pp. 809–814, (2017) [Google Scholar]
  15. B. Emel, S. Mustafa, Video classification based on ConvNet collaboration and feature selection, 25th Signal Processing and Communications Applications Conference (SIU), 15-18 May 2017, pp. 1-4, (2017) [Google Scholar]
  16. D. Koller, and M. Sahami, “Toward optimal feature selection”. In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 284–292, (1996) [Google Scholar]
  17. Rodrigues, L. Pereira and R.Y.M Nakamura, “Wrapper approach for feature selection based on bat algorithm and optimum-path forest”, Expert Systems with Applications, vol.41, pp.2250–2258, (2014) [CrossRef] [Google Scholar]
  18. R.Y.M. Nakamura, L. Pereira, M. Acuckoo, K.A Costa, D. Rodrigues, J.P. Papa, X.S. Yang, “BBA: a binary bat algorithm for feature selection’. in 25th, SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2225 August, IEEE Publication, pp. 291-297, (2012) [CrossRef] [Google Scholar]
  19. A.M Taha, A. Mustapha and S.D. Chen, “Naïve Bayes-Guided bat algorithm for feature selection”, The Scientific World Journal, vol 2013,,(2013) [Google Scholar]

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