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A Framework for Text Classification Using Intuitionistic Fuzzy Sets

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Industrial Engineering, Management Science and Applications 2015

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 349))

Abstract

Due to massively increasing of web pages and online documents, one of crucial processes to handle those documents is automatic (or at least semi-automatic) text classification. Based on the concept of intuitionistic fuzzy set (IFS), a framework for text classification is presented. In the framework, we introduce statistical methods to represent each document as an IFS and to learn a pattern of each document class. Then, a similarity measure for IFSs is applied in order to assign the most relevant class to a new document. The proposed framework with various similarity measures for IFSs is evaluated by benchmark datasets. The experimental results show that our framework yields satisfactory results.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Bangkok, Thailand

    Peerasak Intarapaiboon

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  1. Peerasak Intarapaiboon
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Correspondence to Peerasak Intarapaiboon .

Editor information

Editors and Affiliations

  1. Tokyo University of Science, Tokyo, Japan

    Mitsuo Gen

  2. Convergence Security Department, Kyounggi University, Suwon-si, Korea, Republic of (South Korea)

    Kuinam J. Kim

  3. Dept. Mathematics & Mechanics, University of Science & Technology Beijing (USTB), Beijing, China

    Xiaoxia Huang

  4. Tokyo University of Science, Tokyo, Japan

    Yabe Hiroshi

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Intarapaiboon, P. (2015). A Framework for Text Classification Using Intuitionistic Fuzzy Sets. In: Gen, M., Kim, K., Huang, X., Hiroshi, Y. (eds) Industrial Engineering, Management Science and Applications 2015. Lecture Notes in Electrical Engineering, vol 349. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47200-2_78

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  • DOI: https://doi.org/10.1007/978-3-662-47200-2_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47199-9

  • Online ISBN: 978-3-662-47200-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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