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Rule Induction Based on Rough Sets from Possibilistic Data Tables

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11471))

Abstract

How to induce rules from data tables containing possibilistic information is described under using rough sets based on possible world semantics. A piece of possibilistic information is expressed in a normal and discrete possibility distribution. Under a degree of possibility, the incomplete data table is derived from a possibilistic data table. Rough sets and rules are derived from incomplete data tables. The rough sets and rules obtained under every degree of possibility are aggregated from the viewpoints of certainty and possibility. As a result, rough sets consist of objects with a degree expressed in an interval and an object also supports rules with a degree expressed in an interval value. Furthermore, a criterion is introduced to judge whether or not an object is regarded as validly supporting rules.

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Notes

  1. 1.

    \(\mathcal{E}_{a_{i}}\) is formally \(\mathcal{E}_{a_{i}}(U)\). (U) is usually omitted.

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Author information

Authors and Affiliations

  1. Faculty of Management and Information Science, Josai International University, 1 Gumyo, Togane, Chiba, 283-8555, Japan

    Michinori Nakata

  2. Department of Mathematics and Computer Aided Sciences, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, 804-8550, Japan

    Hiroshi Sakai

Authors
  1. Michinori Nakata
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  2. Hiroshi Sakai
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Corresponding author

Correspondence to Michinori Nakata .

Editor information

Editors and Affiliations

  1. Osaka University, Toyonaka, Osaka, Japan

    Hirosato Seki

  2. Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan

    Canh Hao Nguyen

  3. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Van-Nam Huynh

  4. Osaka University, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

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Nakata, M., Sakai, H. (2019). Rule Induction Based on Rough Sets from Possibilistic Data Tables. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_8

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  • DOI: https://doi.org/10.1007/978-3-030-14815-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14814-0

  • Online ISBN: 978-3-030-14815-7

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