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Using Rough Set to Reduce SVM Classifier Complexity and Its Use in SARS Data Set

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Book cover Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3683))

Abstract

For SVM classifier, Pre-selecting data is necessary to achieve satisfactory classification rate and reduction of complexity. According to Rough Set Theory, the examples in boundary region of a set belong to two or more classes, lying in the boundary of the classes, and according to SVM, support vectors lie in the boundary too. So we use Rough Set Theory to select the examples of boundary region of a set as the SVM classifier set, the complexity of SVM classifier would be reduced and the accuracy maintained. Experiment results of SARS data indicate that our schema is available in both the training and prediction stages.

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References

  1. Steinwart: Sparseness of Support Vector Machines.Some Asymptotically Sharp Bounds. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge

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Author information

Authors and Affiliations

  1. Urban & Rural Construction School, Hebei Agricultural University, 071001, Baoding, China

    Feng Honghai

  2. Information Engineering School, University of Science and Technology Beijing, 100083, Beijing, China

    Feng Honghai & Yang Bingru

  3. China Academy of Traditional Chinese Medicine, 100700, Beijing, China

    Liu Baoyan & Li Ping

  4. Modern Educational Center, Hebei Agricultural University, 071001, Baoding, China

    Yin Cheng

  5. Tian’e Chemical Fiber Company of Hebei Baoding, 071000, Baoding, China

    Chen Yumei

Authors
  1. Feng Honghai
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  2. Liu Baoyan
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  3. Yin Cheng
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  4. Li Ping
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  5. Yang Bingru
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  6. Chen Yumei
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Honghai, F., Baoyan, L., Cheng, Y., Ping, L., Bingru, Y., Yumei, C. (2005). Using Rough Set to Reduce SVM Classifier Complexity and Its Use in SARS Data Set. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_82

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  • DOI: https://doi.org/10.1007/11553939_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28896-1

  • Online ISBN: 978-3-540-31990-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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