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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2000: Knowledge Discovery and Data Mining. Current Issues and New Applications pp 98–109Cite as

Consistency Based Feature Selection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

Abstract

Feature selection is an effective technique in dealing with dimensionality reduction for classification task, a main component of data mining. It searches for an “optimal” subset of features. The search strategies under consideration are one of the three: complete, heuristic, and probabilistic. Existing algorithms adopt various measures to evaluate the goodness of feature subsets. This work focuses on one measure called consistency. We study its properties in comparison with other major measures and different ways of using this measure in search of feature subsets. We conduct an empirical study to examine the pros and cons of these different search methods using consistency. Through this extensive exercise, we aim to provide a comprehensive view of this measure and its relations with other measures and a guideline of the use of this measure with different search strategies facing a new application.

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References

  1. H. Almuallim and T. G. Dietterich. Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69(1–2):279–305, November 1994.

    Article  MATH  MathSciNet  Google Scholar 

  2. M. Ben-Bassat. Pattern recognition and reduction of dimensionality. In P. R. Krishnaiah and L. N. Kanal, editors, Handbook of Statistics, pages 773–791. North Holland, 1982.

    Google Scholar 

  3. A. L. Blum and P. Langley. Selection of relevant features and examples in machine learning. Artificial Intelligence, 97:245–271, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  4. A Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Occam’s razor. Readings in Machine Learning, pages 201–204, 1990.

    Google Scholar 

  5. G. Brassard and P. Bratley. Fundamentals of Algorithms. Prentice Hall, New Jersy, 1996.

    Google Scholar 

  6. M. Dash. Feature selection via set cover. In Proceedings of IEEE Knowledge and Data Engineering Exchange Eorkshop, pages 165–171, Newport, California, November 1997. IEEE Computer Society.

    Google Scholar 

  7. M. Dash and H. Liu. Feature selection methods for classification. Intelligent Data Analysis: An Interbational Journal, 1(3), 1997.

    Google Scholar 

  8. P. A. Devijver and J. Kittler. Pattern Recognition: A Statistical Approach. Prentice Hall, 1982.

    Google Scholar 

  9. G. H. John, R. Kohavi, and K. Pfleger. Irrelevant features and the subset selection problem. In Proceedings of the Eleventh International Conference on Machine Learning, pages 121–129, 1994.

    Google Scholar 

  10. D. S. Johnson. Approximation algorithms for combinatorial problems. Journal of Computer and System Sciences, 9:256–278, 1974.

    Article  MATH  MathSciNet  Google Scholar 

  11. K. Kira and L. A. Rendell. The feature selection problem: Traditional methods and a new algorithm. In Proceedings of Ninth National Conference on AI, pages 129–134, 1992.

    Google Scholar 

  12. R. Kohavi. Wrappers for performance enhancement and oblivious decision graphs. PhD thesis, Department of Computer Science, Stanford University, CA, 1995.

    Google Scholar 

  13. H. Liu, H. Motoda, and M. Dash. A monotonic measure for optimal feature selection. In Proceedings of European Conference on Machine Learning, pages 101–106, 1998.

    Google Scholar 

  14. H. Liu and R. Setiono. Feature selection and classification-a probabilistic wrapper approach. In Proceedings of Ninth International Conference on Industrial and Engineering Applications of AI and ES, 1996.

    Google Scholar 

  15. C. J. Merz and P. M. Murphy. UCI repository of machine learning databases, 1996. FTP from ics.uci.edu in the directory pub/machine-learning-databases.

    Google Scholar 

  16. P. M. Narendra and K. Fukunaga. A branch and bound algorithm for feature selection. IEEE Transactions on Computers, C-26(9):917–922, September 1977.

    Article  Google Scholar 

  17. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, California, 1993.

    Google Scholar 

  18. T. W. Rauber. Inductive Pattern Classification Methods-Features-Sensors. PhD thesis, Department of Electrical Engineering, Universidale Nova de Lisboa, 1994.

    Google Scholar 

  19. W. Siedlecki and J Sklansky. On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence, 2:197–220, 1988.

    Article  Google Scholar 

  20. S. Watanabe. Pattern Recognition: Human and Mechanical. Wiley Intersceince, 1985.

    Google Scholar 

  21. A. Zell and et al. Stuttgart Neural Network Simulator (SNNS), user manual, version 4.1. Technical report, 1995.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, Singapore

    Manoranjan Dash & Huan Liu

  2. Division of Intelligent Sys Sci, Osaka University, Ibaraki, Osaka, 567, Japan

    Hiroshi Motoda

Authors
  1. Manoranjan Dash
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  2. Huan Liu
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  3. Hiroshi Motoda
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Cite this paper

Dash, M., Liu, H., Motoda, H. (2000). Consistency Based Feature Selection. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_12

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  • DOI: https://doi.org/10.1007/3-540-45571-X_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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