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Journal of Applied Mathematics
Volume 2013 (2013), Article ID 350123, 8 pages
http://dx.doi.org/10.1155/2013/350123
Research Article

An Algorithm for Discretization of Real Value Attributes Based on Interval Similarity

1School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China
2State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
3Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway
4College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
5School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA 5005, Australia

Received 3 November 2012; Accepted 25 February 2013

Academic Editor: Xiaojing Yang

Copyright © 2013 Li Zou 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. J. Dougherty, R. Kohavi, and M. Sahami, “Supervised and unsupervised discretization of continuous feature,” in Proceedings of the 12th International Conference on Machine Learning, pp. 194–202, 1995.
  2. X. Liu and H. Wang, “A discretization algorithm based on a heterogeneity criterion,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 9, pp. 1166–1173, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. C. T. Su and J. H. Hsu, “An extended Chi2 algorithm for discretization of real value attributes,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 3, pp. 437–441, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. F. E. H. Tay and L. Shen, “A modified Chi2 algorithm for discretization,” IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 3, pp. 666–670, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. He and S. Hu, “New method for continuous value attribute discretization in rough set theory,” Journal of Nanjing University of Aeronautics and Astronautics, vol. 35, no. 2, pp. 212–215, 2003. View at Google Scholar · View at Scopus
  6. H. S. Nguyen and A. Skowron, “Quantization of real values attributes, rough set and Boolean reasoning approaches,” in Proceedings of the 2nd Joint Annual Conference on Information Science, pp. 34–37, Wrightsville Beach, NC, USA, 1995.
  7. H. Xie, H. Z. Cheng, and D. X. Niu, “Discretization of continuous attributes in rough set theory based on information entropy,” Chinese Journal of Computers, vol. 28, no. 9, pp. 1570–1574, 2005. View at Google Scholar · View at Scopus
  8. J. Y. Ching, A. K. C. Wong, and K. C. C. Chan, “Class-dependent discretization for inductive learning from continuous and mixed-mode data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 7, pp. 641–651, 1995. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Huang, Discretization of continuous attributes for inductive machine learning [M.S. thesis], Department Computer Science, University of Toledo, Toledo, Ohio, USA, 1996.
  10. L. A. Kurgan and K. J. Cios, “CAIM Discretization Algorithm,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 2, pp. 145–153, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. R. Kerber, “ChiMerge: discretization of numeric attributes,” in Proceedings of the 9th National Conference on Artificial Intelligence—AAAI-92, pp. 123–128, AAAI Press, July 1992. View at Scopus
  12. H. Liu and R. Setiono, “Feature selection via discretization,” IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 4, pp. 642–645, 1997. View at Google Scholar · View at Scopus
  13. A. Bondu, M. Boullé, and V. Lemaire, “A non-parametric semi-supervised discretization method,” Knowledge and Information Systems, vol. 24, no. 1, pp. 35–57, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Martínez, D. Ruan, and F. Herrera, “Computing with words in decision support systems: an overview on models and applications,” International Journal of Computational Intelligence Systems, vol. 3, no. 4, pp. 382–395, 2010. View at Google Scholar · View at Scopus
  15. J. Park, H. Bae, and S. Lim, “A DEA-based method of stepwise benchmark target selection with preference, direction and similarity criteria,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 8, pp. 5821–5834, 2012. View at Google Scholar
  16. Z. Pei, Y. Xu, D. Ruan, and K. Qin, “Extracting complex linguistic data summaries from personnel database via simple linguistic aggregations,” Information Sciences, vol. 179, no. 14, pp. 2325–2332, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. Z. Pei, D. Ruan, J. Liu, and Y. Xu, Linguistic Values Based Intelligent Information Processing: Theory, Methods, and Application, vol. 1 of Atlantis Computational Intelligence Systems, Atlantis Press/World Scientific, Singapore, 2009.
  18. M. Arif and S. Basalamah, “Similarity-dissimilarity plot for high dimensional data of different attribute types in biomedical datasets,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 2, pp. 1275–1297, 2012. View at Google Scholar
  19. S. H. Ha, L. Zhuang, Y. Zhou et al., “Treatment method after discretization of continuous attributes based on attributes importance and samples entropy,” in Proceedings of the International Conference on Intelligent Computation Technology and Automation (ICICTA' 11), pp. 1169–1172, Guangdong, China, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Z. Wang and C. R. Dong, “Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy,” IEEE Transactions on Fuzzy Systems, vol. 17, no. 3, pp. 556–567, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. X. Wang, Y. He, L. Dong, and H. Zhao, “Particle swarm optimization for determining fuzzy measures from data,” Information Sciences, vol. 181, no. 19, pp. 4230–4252, 2011. View at Publisher · View at Google Scholar
  22. J. Yu, L. Huang, J. Fu, and D. Mei, “A comparative study of word sense disambiguation of english modal verb by BP neural network and support vector machine,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 5A, pp. 2345–2356, 2011. View at Google Scholar · View at Scopus
  23. C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Liu and X. Xue, “Constructing kernels by fuzzy rules for support vector regressions,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 7A, pp. 4811–4822, 2012. View at Google Scholar
  25. J. Pahasa and I. Ngamroo, “PSO based Kernel principal component analysis and multi-class support vector machine for power quality problem classification,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 3A, pp. 1523–1539, 2012. View at Google Scholar
  26. J. C. Platt, N. Cristianini, and J. Shawe-Taylor, “Large margin DAG’s for multiclass classification,” in Advances in Neural Information Processing Systems, vol. 12, pp. 547–553, MIT Press, Cambridge, Mass, USA, 2000. View at Google Scholar