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Using Rough Set in Feature Selection and Reduction in Face Recognition Problem

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Advances in Knowledge Discovery and Data Mining (PAKDD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3518))

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Abstract

Feature selection and reduction are fundamental steps in pattern recognition problems. The idea of reducts in rough set theory has encouraged many researchers in studying the effectiveness of rough set theory in the problem mentioned above. Through results of experiments in this article, we will show that rough set theory, accompanied by appropriate heuristics, can increase significantly the system’s recognition accuracy.

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References

  1. Komorowski, J., Polkowski, L., Skowron, A.: Rough Sets: A Tutorial

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  2. Swiniarski, R.W.: Rough Set Methods in Feature Reduction and Classification. Int. Appl. Math. Comput. Sci. 11(3), 565–582 (2001)

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  3. Zhong, N., Dong, J., Ohsuga, S.: Using Rough Sets with Heuristic for Feature Selection. Journal of Intelligent Information System 16, 199–214 (2001)

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  4. Hoa, N.S., Son, N.H.: Institute of Computer Sciences, Wasaw University, Poland. Some Efficient Algorithms for Rough Set Methods

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  5. Turk, M., Pentland, A.: Eigenfaces for Recognition (1991)

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  6. LVQ_Pak, Neural Network Research Centre – Laboratory of Computer and Information Science, Helsinki University of Technology, http://www.cis.ut.fi/research/lqv_pak

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, University of Natural Sciences, HCM City, Vietnam

    Le Hoai Bac & Nguyen Anh Tuan

Authors
  1. Le Hoai Bac
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  2. Nguyen Anh Tuan
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Editor information

Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Asahidai 1-1, 923-12292, Nomi, Japan

    Tu Bao Ho

  2. University of Hong Kong, Pokfulam Road, Hong Kong, China

    David Cheung

  3. Department of Computer Science and Engineering, Arizona State University, Tempe, Arizona, USA

    Huan Liu

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© 2005 Springer-Verlag Berlin Heidelberg

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Bac, L.H., Tuan, N.A. (2005). Using Rough Set in Feature Selection and Reduction in Face Recognition Problem. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_28

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  • DOI: https://doi.org/10.1007/11430919_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26076-9

  • Online ISBN: 978-3-540-31935-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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