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The Scientific World Journal
Volume 2014, Article ID 972125, 8 pages
http://dx.doi.org/10.1155/2014/972125
Research Article

An Improved Feature Selection Based on Effective Range for Classification

1College of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, China
2National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130000, China
3Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130000, China
4School of Mathematics and Statistics, Northeast Normal University, Changchun 130000, China

Received 29 August 2013; Accepted 2 December 2013; Published 4 February 2014

Academic Editors: C.-C. Chang and J. Shu

Copyright © 2014 Jianzhong Wang 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. E. Xing, M. Jordan, and R. Karp, “Feature selection algorithms for classification and clustering,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, pp. 1–12, 2005. View at Publisher · View at Google Scholar
  2. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar
  3. Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Lazar, J. Taminau, S. Meganck et al., “A survey on filter techniques for feature selection in gene expression microarray analysis,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 4, pp. 1106–1119, 2012. View at Google Scholar
  5. J. R. Quinlan, “Learning efficient classification procedures and their application to chess end games,” in Machine Learning: An Artificial Intelligence Approach, pp. 463–482, Morgan Kaufmann, San Francisco, Calif, USA, 1983. View at Google Scholar
  6. J. R. Quinlan, C4.5: Programs For Machine Learning, Morgan Kaufmann, San Francisco, Calif, USA, 1993.
  7. L. Breiman, J. H. Friedman et al., Classification and Regression Trees, Wadsforth International Group, 1984.
  8. J. Kittler, “Feature set search algorithms,” in Pattern Recognition and Signal Processing, C. H. Chen, Ed., pp. 41–60, Sijthoff and Noordhoff, The Netherlands, 1978. View at Google Scholar
  9. M. M. Kabir, M. M. Islam, and K. Murase, “A new wrapper feature selection approach using neural network,” Neurocomputing, vol. 73, no. 16–18, pp. 3273–3283, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. J. X. Ye and X. L. Gong, “A novel fast Wrapper for feature subset selection,” Journal of Changsha University of Science and Technology, vol. 7, no. 4, pp. 69–73, 2010. View at Google Scholar
  11. J. Wang, L. Wu, J. Kong, Y. Li, and B. Zhang, “Maximum weight and minimum redundancy: a novel framework for feature subset selection,” Pattern Recognition, vol. 46, no. 6, pp. 1616–1627, 2013. View at Google Scholar
  12. L. J. Van't Veer, H. Dai, M. J. Van de Vijver et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, vol. 415, no. 6871, pp. 530–536, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, Max-relevance, and Min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Liu, J. Li, and L. Wong, “A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns,” Genome Informatics Series, vol. 13, pp. 51–60, 2002. View at Google Scholar · View at Scopus
  15. K. Kira and L. A. Rendell, “A practical approach to feature selection,” in Proceedings of the 9th International Conference on Machine Learning, pp. 249–256, 1992.
  16. I. Kononenko, “Estimating features: analysis and extension of RELIEF,” in Proceedings of the 6th European Conference on Machine Learning, pp. 171–182, 1994.
  17. M. A. Hall, Correlation-based feature selection for machine learning [Ph.D. thesis], University of Waikato, Hamilton, New Zealand, 1999.
  18. C. Ding and H. Peng, “Minimum redundancy feature selection from microarray gene expression data,” Journal of Bioinformatics and Computational Biology, vol. 3, no. 2, pp. 185–205, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Almuallim and T. Dietterich, “Learning with many irrelevant features,” in Proceedings of the 9th National Conference on Artificial Intelligence, pp. 547–552, San Jose, 1991.
  20. B. Chandra and M. Gupta, “An efficient statistical feature selection approach for classification of gene expression data,” Journal of Biomedical Informatics, vol. 44, no. 4, pp. 529–535, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Härdle and L. Simar, Applied Multivariate Statistical Analysis, Springer, 2007.
  22. S. A. Armstrong, J. E. Staunton, and L. B. Silverman, “MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia,” Nature Genetics, vol. 30, no. 1, pp. 41–47, 2002. View at Publisher · View at Google Scholar
  23. A. Alizadeh et al., “Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling,” Nature, vol. 403, pp. 503–511, 2000. View at Publisher · View at Google Scholar
  24. Chemosensitivity prediction by transcriptional profiling, Whitehead Massachusetts Institute of Technology Center For Genome Research, Cambridge, Mass, USA.
  25. T. R. Golub, D. K. Slonim, P. Tamayo et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, no. 5439, pp. 531–527, 1999. View at Publisher · View at Google Scholar · View at Scopus