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New Fuzzy Decision Tree Model for Text Classification

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The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 407))

Abstract

In this paper, a supervised automatic text documents classification using the fuzzy decision trees technique is proposed. Whatever the algorithm used in the fuzzy decision trees, there must be a criterion for the choice of discriminating attribute at the nodes to partition. For fuzzy decision trees two heuristics are usually used to select the discriminating attribute at the node to partition. In the field of text documents classification there is a heuristic that has not yet been tested. This paper tested this heuristic.

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Author information

Authors and Affiliations

  1. Kingdom of Saudi Arabia, Taif University, Taif, Saudi Arabia

    Ben Abdessalam Wahiba

  2. Higher Institute of Management, Tunis University, Tunis, Tunisia

    Ben El Fadhl Ahmed

Authors
  1. Ben Abdessalam Wahiba
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  2. Ben El Fadhl Ahmed
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Corresponding author

Correspondence to Ben El Fadhl Ahmed .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  2. Department of Information Technology, Cairo University Faculty of Computers & Information, Giza, Egypt

    Aboul Ella Hassanien

  3. College of Management and Technology, Arab Academy for Science, Technology, and Maritime Transport, Giza, Egypt

    Nashwa El-Bendary

  4. Bengal College of Engineering and Technology, Durgapur, West Bengal, India

    Nilanjan Dey

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© 2016 Springer International Publishing Switzerland

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Wahiba, B.A., Ahmed, B.E.F. (2016). New Fuzzy Decision Tree Model for Text Classification. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_28

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  • DOI: https://doi.org/10.1007/978-3-319-26690-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26688-6

  • Online ISBN: 978-3-319-26690-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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