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Mathematical Problems in Engineering
Volume 2015, Article ID 839081, 14 pages
http://dx.doi.org/10.1155/2015/839081
Research Article

An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

1School of Mathematics and Statistics, Xidian University, China
2School of Science, Xi’an University of Science and Technology, China

Received 15 January 2015; Revised 26 May 2015; Accepted 28 May 2015

Academic Editor: Hak-Keung Lam

Copyright © 2015 Mengling Zhao and Hongwei Liu. 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. J. E. Hunt and D. E. Cooke, “Learning using an artificial immune system,” Journal of Network and Computer Applications, vol. 19, no. 2, pp. 189–212, 1996. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Tang, T. Yamaguchi, K. Tashima, O. Ishizuka, and K. Tanno, “Multiple-valued immune network model and its simulations,” in Proceedings of the 27th International Symposium on Multiple-Valued Logic, pp. 233–238, Antigonish, Canada, May 1997. View at Scopus
  3. L. N. de Castro and F. J. von Zuben, “An evolutionary immune network for data clustering,” in Proceedings of the 6th Brazilian Symposium on Neural Networks (SBRN '00), pp. 84–89, IEEE, Rio de Janeiro, Brazil, 2000. View at Publisher · View at Google Scholar
  4. L. N. de Castrol and F. J. von Zuben, “Immune neural network models: theoretical and emprical comparisons,” International Journal of Computational Intelligence and Applications, vol. 1, no. 3, pp. 239–257, 2001. View at Publisher · View at Google Scholar
  5. D. Fu, X. Yu, and T. Wang, “Segmentation algorithm study for infrared images with occluded target based on artificial immune system,” in Proceedings of the 8th International Conference on Computational Intelligence and Security (CIS '12), pp. 350–353, Guangzhou, China, November 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Fu, X. Yu, and H. Tong, “Target extraction of blurred infrared image with an immune network template algorithm,” Journal of Optics and Laser Technology, vol. 56, pp. 102–106, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Yu, “Blurred trace infrared image segmentation based on template approach and immune factor,” Journal of Infrared Physics and Technology, vol. 67, pp. 116–120, 2014. View at Publisher · View at Google Scholar
  8. A. Watkins, J. Timmis, and L. Boggess, “Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm,” Genetic Programming and Evolvable Machines, vol. 5, no. 3, pp. 291–317, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Watkins and J. Timmis, “Artificial immune recognition system (AIRS): revisions and refinements,” in Proceedings of the ICARIS, pp. 173–181, Springer, 2002.
  10. A. Watkins and J. Timmis, “Exploiting parallelism inherent in AIRS, an artificial immune classifier,” in Artificial Immune Systems: Third International Conference, ICARIS 2004, Catania, Sicily, Italy, September 13–16, 2004. Proceedings, vol. 3239 of Lecture Notes in Computer Science, pp. 427–438, Springer, Berlin, Germany, 2004. View at Publisher · View at Google Scholar
  11. L. N. de Castro and F. J. von Zuben, “Artificial immune systems: a novel approach to pattern recognition,” in Artificial Neural Networks in Pattern Recognition, pp. 67–84, University of Paisley, Renfrewshire, UK, 2002. View at Google Scholar
  12. R. Liu, M. Niu, and L. Jiao, “A new artificial immune network classifier for SAR image,” in MIPPR 2009: Pattern Recognition and Computer Vision, vol. 7496 of Proceedings of SPIE, pp. 1–8, Yichang, China, October 2009. View at Publisher · View at Google Scholar
  13. W. Li, H. Huang, C. Wang, and H. Tang, “Synthetic fault diagnosis method of power transformer based on rough set theory and improved artificial immune network classification algorithm,” in Proceedings of the 4th International Conference on Natural Computation (ICNC '08), pp. 676–681, IEEE, Jinan, China, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Danzhen, A. Qian, and C. Chen, “The application of artificial immune network in load classification,” in Proceedings of the 3rd International Conference on Deregulation and Restructuring and Power Technologies (DRPT '08), pp. 1394–1398, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Bhaduri, “University time table scheduling using genetic artificial immune network,” in Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom ’09), pp. 289–292, Kottayam, India, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Khaleghi, M. M. Farsangi, H. Nezamabadi-pour, and K. Y. Lee, “Voltage stability improvement by multi-objective placement of SVC using modified artificial immune network algorithm,” in Proceedings of the IEEE Power & Energy Society General Meeting (PES '09), pp. 1–7, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Zhong, L. Zhang, B. Huang, and P. Li, “An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 2, pp. 420–431, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. F. O. de Franca, F. J. Von Zuben, and L. N. de Castro, “An artificial immune network for multimodal function optimization on dynamic environments,” in Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO '05), pp. 289–296, New York, NY, USA, 2005.
  19. A.-L. Chen and Q. Guo, “An effective hybrid optimization algorithm based on self-adaptive particle swarm optimization algorithm and artificial immune clone algorithm,” in Proceedings of the 4th International Conference on Natural Computation (ICNC '08), pp. 129–132, IEEE, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. T. D. Do, S. C. Hui, A. C. M. Fong, and B. Fong, “Associative classification with artificial immune system,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 217–228, 2009. View at Publisher · View at Google Scholar
  21. C. L. Blake and C. J. Merz, UCI Repository of Machine Learning Databases, http://archive.ics.uci.edu/ml/datasets.html.
  22. D. A. Clausi and B. Yu, “Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 1, pp. 215–228, 2004. View at Publisher · View at Google Scholar · View at Scopus