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YUAN Hanning, WANG Shuliang, LI Ying, FAN Jinghua. Feature Selection with Data Field[J]. Chinese Journal of Electronics, 2014, 23(4): 661-665.
Citation: YUAN Hanning, WANG Shuliang, LI Ying, FAN Jinghua. Feature Selection with Data Field[J]. Chinese Journal of Electronics, 2014, 23(4): 661-665.
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Feature Selection with Data Field

  • 1.

    School of Software, Beijing Institute of Technology, Beijing 100081, China;

  • 2.

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430072, China;

  • 3.

    International School of Software, Wuhan University, Wuhan 430072, China

Funds: This paper is supported by the the National Natural Science Foundation of China (No.61173061, No.71201120), and the Doctoral Fund of Higher Education (No.20121101110036).
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  • Received Date: June 30, 2013
  • Revised Date: August 31, 2013
  • Published Date: October 04, 2014
    Graphical Abstract
    • Abstract
  • A new feature selection method is proposed for high-dimensional data clustering on the basis of data field. With the potential entropy to evaluate the importance of feature subsets, features are filtered by removing unimportant features or noises from the original datasets. Experiments show that the proposed method can sharply reduce the number of dimensions and effectively improve the clustering performance on WDBC dataset.
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    • References (7)
  • D.R. Li, S.L. Wang and D.Y. Li, Spatial Data Mining Theories and Applications (second edition), Science Press, Beijing, China, pp.2-36, 2013. (in Chinese)
    S.L. Wang, W.Y. Gan, D.Y. Li and D.R. Li, Data field for hierarchical clustering, International Journal of Data Warehousing and Mining, Vol.7, No.2, pp.43-63, 2011.
    I. Guyon and A. Elisseeff, An introduction to variable and feature selection, The Journal of Machine Learning Research, Vol.3, pp.1157-1182, 2003.
    R. Kohavi and G.H. John, Wrappers for feature subset selection, Artificial Intelligence, Vol.97, No.1-2, pp.273-324, 1997.
    J. Zhong, Q. Sun, X. Li and L. Wen, A novel feature selection method based on probability latent semantic analysis for chinese text classification, Chinese Journal of Electronics, Vol.20, Vol.2, pp.228-232, 2011.
    Y. Sun, S. Todorovic and S. Goodison, Local-learning-based feature selection for high-dimensional data analysis, IEEE Transactions on Pattern Analysis Machince Intelligence, Vol.32, No.9, pp.1610-1626, 2010.
    P.S. Bradley, K.P. Bennett and A. Demiriz, Constrained K-Means clustering, MSR-TR-2000-65, 2000.
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