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An Accelerator of Feature Selection Applying a General Fuzzy Rough Model

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Advances in Knowledge Discovery and Data Mining (PAKDD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11440))

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Abstract

Feature selection, also known as variable selection or attribute reduction, is to select a subset relevant features to speedup learning/mining and to improve the learning/mining quality. In the big data era, some feature selection methods have to face the running time problem led by the large-scale data. As a result, in this paper, we try to narrow this gap by proposing a feature selection accelerator. Considering fuzzy rough techniques need no extra expert knowledge, we design the feature selection accelerator based on fuzzy rough reduction techniques. First, we proposed a fuzzy rough accelerator by deleting the learned/discernible instances in the process of feature selection, which decreases the computation and accelerates feature selection. Second, we design a fuzzy rough based feature selection accelerated algorithm. Finally, the numerical experiments demonstrate that the proposed accelerated algorithm could obtain the same reduction results and save much more time, especially on the large-scale datasets.

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Acknowledgements

This work is supported by National Key Research & Develop Plan (No. 2016YFB1000702), National Key R&D Program of China (2017YFB1400700), and NSFC under the grant No. 61732006, 61532021, 61772536, 61772537, 61702522 and NSSFC (No. 12\&ZD220), and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (15XNLQ06). It was partially done when the authors worked in SA Center for Big Data Research in RUC. This Center is funded by a Chinese National 111 Project Attracting. This work is also supported by the Macao Science and Technology Development Fund (081/2015/A3).

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Authors and Affiliations

  1. Key Lab of Data Engineering and Knowledge Engineering of MOE, Renmin University of China, Beijing, People’s Republic of China

    Peng Ni, Suyun Zhao, Hong Chen & Cuiping Li

  2. Information of School, Renmin University of China, Beijing, People’s Republic of China

    Peng Ni, Suyun Zhao, Hong Chen & Cuiping Li

Authors
  1. Peng Ni
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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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Corresponding author

Correspondence to Suyun Zhao .

Editor information

Editors and Affiliations

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

    Qiang Yang

  2. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  3. University of Macau, Taipa, Macau, China

    Zhiguo Gong

  4. Southeast University, Nanjing, China

    Min-Ling Zhang

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sheng-Jun Huang

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Ni, P., Zhao, S., Chen, H., Li, C. (2019). An Accelerator of Feature Selection Applying a General Fuzzy Rough Model. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_47

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  • DOI: https://doi.org/10.1007/978-3-030-16145-3_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16144-6

  • Online ISBN: 978-3-030-16145-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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