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GA Based Optimal Keyword Extraction in an Automatic Chinese Web Document Classification System

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Frontiers of High Performance Computing and Networking ISPA 2007 Workshops (ISPA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4743))

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Abstract

The main steps for designing an automatic document classification system include feature extraction and classification. In this paper a method to improve feature extraction is proposed. In this method, genetic algorithm (GA) was applied to determine the threshold values of four criteria for extracting the representative keywords for each class. The purpose of these four threshold values is to extract as few representative keywords as possible. This keyword extraction method was combined with two classification algorithms, vector space model (VSM) and support vector machine (SVM), for examining the performance of the proposed classification system under various extracting conditions.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Chung Hua University, No.707, Sec.2, WuFu Rd., Hsinchu, 300 Taiwan, R.O.C.

    Chih-Hsun Chou & Ya-Hui Chen

  2. Department of Computer Science and Information Engineering, National United University, Miaoli, Taiwan, R.O.C.

    Chin-Chuan Han

Authors
  1. Chih-Hsun Chou
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  2. Chin-Chuan Han
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  3. Ya-Hui Chen
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Editor information

Parimala Thulasiraman Xubin He Tony Li Xu Mieso K. Denko Ruppa K. Thulasiram Laurence T. Yang

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© 2007 Springer-Verlag Berlin Heidelberg

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Chou, CH., Han, CC., Chen, YH. (2007). GA Based Optimal Keyword Extraction in an Automatic Chinese Web Document Classification System. In: Thulasiraman, P., He, X., Xu, T.L., Denko, M.K., Thulasiram, R.K., Yang, L.T. (eds) Frontiers of High Performance Computing and Networking ISPA 2007 Workshops. ISPA 2007. Lecture Notes in Computer Science, vol 4743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74767-3_24

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  • DOI: https://doi.org/10.1007/978-3-540-74767-3_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74766-6

  • Online ISBN: 978-3-540-74767-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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