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Research on Attribute Reduction Method Based on Local Dependency

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Learning Technologies and Systems (SETE 2020, ICWL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12511))

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Abstract

Attribute reduction is one of the research hotspots in the field of data mining. Although the result of attribute reduction algorithm based on single attribute identification matrix is better, it is still not efficient enough to deal with large-scale information system problems. In this paper, the concept of sub matrix of single attribute identification matrix is proposed. Based on the sub matrix, the calculation method of local dependency degree is given, and an attribute reduction algorithm based on local dependency degree is designed. If the equivalence class of information system is regarded as basic knowledge granules, this algorithm first finds an attribute set to separate the first particle from other particles, and then adds attributes to the attribute set in order to separate the second particle from other particles. Repeat the above operation until all particles are distinguished, and the resulting attribute set is called reduction set. This algorithm reduces the time and space complexity of reduction algorithm to a certain extent. The effectiveness of this method is verified by UCI data set, which provides a method for attribute reduction.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (71771078, 71371064).

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Authors and Affiliations

  1. School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, 050018, China

    Xiaozheng Yang & Fachao Li

  2. College of Science, Hebei University of Science and Technology, Shijiazhuang, 050018, China

    Yexing Ren & Fachao Li

Authors
  1. Xiaozheng Yang
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  2. Yexing Ren
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  3. Fachao Li
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Corresponding author

Correspondence to Yexing Ren .

Editor information

Editors and Affiliations

  1. Ningbo Institute of Industrial Technology, Ningbo, China

    Chaoyi Pang

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Monash University, Clayton, VIC, Australia

    Guanliang Chen

  4. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania

    Elvira Popescu

  5. Department of Physics, Zhejiang University, Hangzhou, China

    Lu Chen

  6. School of Computer Science, South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Zhejiang University, Ningbo, China

    Bailing Zhang

  8. Universidad Internacional De La Rioja, Logroño, Spain

    Silvia Margarita Baldiris Navarro

  9. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

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Yang, X., Ren, Y., Li, F. (2021). Research on Attribute Reduction Method Based on Local Dependency. In: Pang, C., et al. Learning Technologies and Systems. SETE ICWL 2020 2020. Lecture Notes in Computer Science(), vol 12511. Springer, Cham. https://doi.org/10.1007/978-3-030-66906-5_13

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  • DOI: https://doi.org/10.1007/978-3-030-66906-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66905-8

  • Online ISBN: 978-3-030-66906-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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