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Mathematical Problems in Engineering
Volume 2014, Article ID 706178, 8 pages
http://dx.doi.org/10.1155/2014/706178
Research Article

Optimization ELM Based on Rough Set for Predicting the Label of Military Simulation Data

Science and Technology on Information Systems Engineering Laboratory, Nanjing 210007, China

Received 19 April 2014; Revised 26 July 2014; Accepted 18 August 2014; Published 25 September 2014

Academic Editor: Yi Jin

Copyright © 2014 Xiao-jian Ding and Ming Lei. 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. W. Liguo, X. Qing, and M. Xianquan, “The research of the data VV&C for the equipment war gaming based on the HLA,” Computer Engineering and Applications, vol. 21, pp. 200–202, 2006. View at Google Scholar
  2. M.-Z. Li, J.-K. Zhang, and W.-F. Che, “Research on data for combat simulation,” Command Control & Simulation, vol. 32, no. 4, pp. 71–74, 2010. View at Google Scholar
  3. H. Gao, H. Zhang, G. Chen et al., “Research and implementation of military simulation,” Fire Control & Command Control, vol. 34, no. 2, pp. 150–153, 2009. View at Google Scholar
  4. W.-M. Zhang and Q. Xue, “Application of rough set in date mining of warfare simulation,” Journal of System Simulation, vol. 18, no. 2, pp. 179–181, 2006. View at Google Scholar
  5. G.-B. Huang, X. Ding, and H. Zhou, “Optimization method based extreme learning machine for classification,” Neurocomputing, vol. 74, no. 1-3, pp. 155–163, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. G.-B. Huang and C.-K. Siew, “Extreme learning machine: RBF network case,” in Proceedings of the International Conference on Control, Automation, Robotics and Vision, pp. 1651–1663, 2004.
  7. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. G.-B. Huang, L. Chen, and C.-K. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879–892, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. J. W. Cao, T. Chen, and J. Fan, “Fast online learning algorithm for landmark recognition based on BoW framework,” in Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou, China, June 2014.
  10. J. W. Cao and L. Xiong, “Protein sequence classification with improved extreme learning machine algorithms,” BioMed Research International, vol. 2014, Article ID 103054, 12 pages, 2014. View at Publisher · View at Google Scholar
  11. J. W. Cao, Z. Lin, G.-B. Huang, and N. Liu, “Voting based extreme learning machine,” Information Sciences, vol. 185, pp. 66–77, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  13. P. L. Bartlett, “The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network,” IEEE Transactions on Information Theory, vol. 44, no. 2, pp. 525–536, 1998. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. Z. Pawlak, J. Grzymala-Busse, R. Slowinski, and W. Ziarko, “Rough sets,” Communications of the ACM, vol. 38, no. 11, pp. 88–95, 1995. View at Publisher · View at Google Scholar · View at Scopus
  15. Z. Pawlak and A. Skowron, “Rudiments of rough sets,” Information Sciences, vol. 177, no. 1, pp. 3–27, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. S. Greco, M. Benedetto, and R. Slowinski, “New developments in the rough set approach to multi-attribute decision analysis,” Bulletin of International Rough Set Society, vol. 2, no. 2-3, pp. 57–87, 1998. View at Google Scholar
  17. J. Dougherty, R. Kohavi, and M. Sahami, “Supervised and unsuperivised discretization of continuous features,” in Proceedings of the 12th International Conference on Machine Learning, pp. 194–202, 1995.
  18. R. kerber, “Discretization of numeric attributes,” in Proceedings of the 10th National Conference on Artificial Intelligence, pp. 123–128, MIT Press, Cambrige, Mass, USA, 1992.
  19. F. Hussain, H. Liu, C. L. Tan, and M. Dash, “Discretization: an enabling technique,” Tech. Rep., School of Computing, Singapore, 1999. View at Google Scholar
  20. S. D. Bay, “Multivariate discretization of continuous variables for set mining,” in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 315–319, August 2000. View at Scopus
  21. T. C. Lei, S. Wan, and T. Y. Chou, “The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification—a case study on rice classification, Taiwan,” Computational Geosciences, vol. 12, no. 1, pp. 1–14, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. The Spider Library for MATLAB, http://www.kyb.tuebingen.mpg.de/bs/people/spider/.
  23. N. Ancona, C. Cicirelli, E. Stella, and A. Distante, “Object detection in images: run-time complexity and parameter selection of support vector machines,” in Proceedings of the 16th International Conference on Pattern Recognition, vol. 2, pp. 426–429, August 2002. View at Publisher · View at Google Scholar
  24. P. Ghanty, S. Paul, and N. R. Pal, “NEUROSVM: an architecture to reduce the effect of the choice of kernel on the performance of SVM,” Journal of Machine Learning Research, vol. 10, no. 3, pp. 591–622, 2009. View at Google Scholar · View at Scopus