Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017, Article ID 3956415, 7 pages
https://doi.org/10.1155/2017/3956415
Research Article

A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era

1College of Computer Science, Sichuan University, Chengdu 610065, China
2College of Cybersecurity, Sichuan University, Chengdu 610065, China
3Chongqing University of Technology, Chongqing 400054, China
4Chengdu University of Information Technology, Chengdu 610225, China

Correspondence should be addressed to Wen Chen; nc.ude.ucs@nehcnew and Hanli Yang; nc.ude.tuqc@lhy

Received 2 February 2017; Accepted 3 May 2017; Published 12 June 2017

Academic Editor: Zonghua Zhang

Copyright © 2017 Fangdong Zhu et al. 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. S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri, “Self-nonself discrimination in a computer,” in Proceedings of the IEEE Symposium on Research in Security and Privacy, (SP '94), pp. 202–212, IEEE Computer Society, Oakland, May 1994. View at Scopus
  2. F. González, D. Dasgupta, and L. F. Niño, “A randomized real-valued negative selection algorithm,” in In Proceedings of the 2nd International Conference on Artificial Immune Systems, vol. 2787, pp. 261–272, 2003.
  3. Z. Ji and D. Dasgupta, “Real-valued negative selection algorithm with variable-sized detectors,” in Genetic and Evolutionary Computation Conference, vol. 3102 of Lecture Notes in Computer Science, pp. 287–298, Springer, Berlin, Heidelberg, 2004. View at Publisher · View at Google Scholar
  4. Z. Ji and D. Dasgupta, “V-detector: an efficient negative selection algorithm with 'probably adequate' detector coverage,” Information Sciences, vol. 179, no. 10, pp. 1390–1406, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Cui, D. Pi, and C. Chen, “BIORV-NSA: Bidirectional inhibition optimization r-variable negative selection algorithm and its application,” Applied Soft Computing Journal, vol. 32, pp. 544–552, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Sanchez-Gutierrez, M. Tozluoglu, J. D. Barry, A. Pascual, Y. Mao, and L. M. Escudero, “Fundamental physical cellular constraints drive self-organization of tissues,” EMBO Journal, vol. 35, no. 1, pp. 77–88, 2016. View at Publisher · View at Google Scholar · View at Scopus
  7. H. W. Sheng, W. Luo, F. Alamgir, J. Bai, and E. Ma, “Atomic packing and short-to-medium-range order in metallic glasses,” Nature, vol. 439, pp. 419–425, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Zhao, K. Xuan, W. Rahayu et al., “Voronoi-based continuous nearest neighbor search in mobile navigation,” IEEE Transactions on Industrial Electronics, vol. 58, no. 6, pp. 2247–2257, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. M. de Berg, O. Cheong, M. van Kreveld, and M. Overmars, Computational Geometry: Algorithms and Applications, Springer, 2008, https://www.amazon.com/Computational-Geometry-Applications-Mark-Berg/dp/3540779736.
  10. B. Chazelle, “An optimal convex hull algorithm and new results on cuttings,” in Proceedings of the 32nd Annual Symposium on Foundations of Computer Science, pp. 29–38, October 1991. View at Scopus
  11. K. L. Clarkson and P. W. Shor, “Applications of random sampling in computational geometry, II,” Discrete & Computational Geometry, vol. 4, no. 1, pp. 387–421, 1989. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Seidel, “Small-dimensional linear programming and convex hulls made easy,” Discrete & Computational Geometry, vol. 6, no. 1, pp. 423–434, 1991. View at Publisher · View at Google Scholar · View at Scopus
  13. W. Chen, T. Li, X. Liu, and B. Zhang, “A negative selection algorithm based on hierarchical clustering of self set,” Science China Information Sciences, vol. 56, no. 8, pp. 1–13, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  14. Y. Chen, X. S. Zhou, and T. S. Huang, “One-class SVM for learning in image retrieval,” in Proceedings of IEEE International Conference on Image Processing (ICIP) 2001, pp. 34–37, grc, October 2001. View at Scopus
  15. C.-C. Chang and C.-J. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Herceg, M. Kvasnica, C. Jones, and M. Morari, “Multi-parametric toolbox 3.0,” in Proceedings of the 12th European Control Conference, (ECC '13), pp. 502–510, Zurich, Switzerland, July 2013. View at Scopus