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An United Extended Rough Set Model Based on Developed Set Pair Analysis Method

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Artificial Intelligence and Computational Intelligence (AICI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5855))

Abstract

Different with traditional set pair analysis method, a new method to micro-decompound the discrepancy degree is proposed in line with the distributing actuality of missing attributes. Integrated with rough set theory, advances the united set pair tolerance relation and gives corresponding extended rough set model. Different values of identity degree and discrepancy degree can modulate the performance of this model and extend it’s application range. Then expound some existed extended rough set model are the subschema of it. At last simulation experiments and conclusion are given, which validate the united set pair tolerance relation model can improve classification capability.

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Author information

Authors and Affiliations

  1. Key Lab of Ministry of Education for CI & SP, Anhui University, Hefei, Anhui, China

    Xia Ji, Longshu Li, Shengbing Chen & Yi Xu

Authors
  1. Xia Ji
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  2. Longshu Li
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  3. Shengbing Chen
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  4. Yi Xu
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Editor information

Editors and Affiliations

  1. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  2. College of Metrological Technology and Engineering, China Jiliang University, 310018, Hangzhou, Zhejiang Province, China

    Lanzhou Wang

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China

    Fu Lee Wang

  4. College of Information Science and Technology, Hainan University, 570228, Haikou, China

    Jingsheng Lei

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© 2009 Springer-Verlag Berlin Heidelberg

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Ji, X., Li, L., Chen, S., Xu, Y. (2009). An United Extended Rough Set Model Based on Developed Set Pair Analysis Method. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_2

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  • DOI: https://doi.org/10.1007/978-3-642-05253-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05252-1

  • Online ISBN: 978-3-642-05253-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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