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Fuzzy Rough Incremental Attribute Reduction Applying Dependency Measures

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10366))

Abstract

Since data increases with time and space, many incremental rough based reduction techniques have been proposed. In these techniques, some focus on knowledge representation on the increasing data, some focus on inducing rules from the increasing data. Whereas there is less work on incremental feature selection (i.e., attribute reduction) from the increasing data, especially the increasing real valued data. And fuzzy rough sets is then applied in this incremental method because fuzzy rough set can effectively reduce attributes from the real valued data. By analyzing the basic concepts, such as lower approximation and positive region, of fuzzy rough sets on incremental datasets, the incremental mechanisms of these concepts are then proposed. An incremental algorithm is then designed. Finally, some numerical experiments demonstrate that the incremental algorithm is effective and efficient compared to non-incremental attribute reduction algorithms, especially on the datasets with large number of attributes.

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Author information

Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Yangming Liu, Suyun Zhao, Hong Chen & Cuiping Li

  2. Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China, MOE, Beijing, China

    Suyun Zhao

  3. Building 6, 1388 Zhangdong Road Pudong New Area, Shanghai, 201203, People’s Republic of China

    Yanmin Lu

Authors
  1. Yangming Liu
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  2. Suyun Zhao
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  3. Hong Chen
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  4. Cuiping Li
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  5. Yanmin Lu
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Corresponding author

Correspondence to Suyun Zhao .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University, Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Liu, Y., Zhao, S., Chen, H., Li, C., Lu, Y. (2017). Fuzzy Rough Incremental Attribute Reduction Applying Dependency Measures. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_37

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  • DOI: https://doi.org/10.1007/978-3-319-63579-8_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63578-1

  • Online ISBN: 978-3-319-63579-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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