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Attribute Reduction in Decision-Theoretic Rough Set Model Using MapReduce

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Rough Sets and Knowledge Technology (RSKT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8818))

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Abstract

Attribute reduction is one of the most important research issues in decision-theoretic rough set model. This paper studies a new attribute measure preserving boundary region partition for a reduct. The relationships among the positive region, the probabilistic positive region and the indiscernibility object pairs for an equivalence class are analyzed. A heuristic attribute reduction algorithm framework using MapReduce in decision-theoretic rough set model is proposed. This study gives some insights into how to conduct attribute reduction in decision-theoretic rough set for big data.

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Author information

Authors and Affiliations

  1. Key Laboratory of Cloud Computing & Intelligent Information Processing, Jiangsu University of Technology, 213001, Changzhou City, P.R. China

    ** Qian, ** Lv & Qingjun Guo

  2. School of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, P.R. China

    ** Qian, ** Lv & Qingjun Guo

  3. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, P.R. China

    **aodong Yue

Authors
  1. ** Qian
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  2. ** Lv
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  3. Qingjun Guo
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  4. **aodong Yue
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Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    Duoqian Miao

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. University of Warsaw, Warsaw, Poland

    Dominik Ślȩzak

  4. University of Applied Sciences, München, Germany

    Georg Peters

  5. Tian** University, Tian**, China

    Qinghua Hu

  6. Tongji University, Shanghai, China

    Ruizhi Wang

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Qian, J., Lv, P., Guo, Q., Yue, X. (2014). Attribute Reduction in Decision-Theoretic Rough Set Model Using MapReduce. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_55

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  • DOI: https://doi.org/10.1007/978-3-319-11740-9_55

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11739-3

  • Online ISBN: 978-3-319-11740-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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