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Attribute Reduction: An Ensemble Strategy

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Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

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Abstract

In rough set theory, the heuristic strategy for computing reducts does not take the stability of the selected attributes into account. An unstable reduct may imply the lower adaption to data variations. To fill such a gap, an ensemble strategy is embedded in heuristic algorithm for achieving stable reducts of variable precision fuzzy rough sets. Given an admissible error \(\beta \), for each looping in the algorithm, a set of attributes will be chosen through considering several admissible errors around the given \(\beta \), instead of choosing only one attribute by \(\beta \) itself. The main purpose of this replacement is to simulate the sample variations through slight changing of admissible errors over the fixed data. Consequently, the voting ensemble can be used to select an attribute with the maximal frequency of occurrences. The experimental results on eight UCI data sets demonstrate that our ensemble strategy based heuristic approach will improve the stabilities of reducts effectively, while it is unnecessary to add too many attributes for constructing the reducts. This study suggests new trends for considering robust problems in the framework of rough set.

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Acknowledgments

This work is supported by the Natural Science Foundation of China (Nos. 61572242, 61503160, 61502211), Macau Science and Technology Development Foundation (No. 081/2015/A3), Postgraduate Innovation Foundation of Jiangsu Province (No. KYLX16_0505), Postgraduate Research Innovation Foundation of Jiangsu University of Science and Technology (No. YCX15S-10), Qing Lan Project of Jiangsu Province of China, Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province (No. 2014002).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, People’s Republic of China

    Suping Xu, Xibei Yang & Xiangjian Chen

  2. Intelligent Information Processing Key Laboratory of Shanxi Province, Shanxi University, Taiyuan, 030006, Shanxi, People’s Republic of China

    Suping Xu & Xibei Yang

  3. School of Mathematics and Physics, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, People’s Republic of China

    Pingxin Wang

  4. Faculty of Science, Kunming University of Science and Technology, Kunming, 650500, Yunnan, People’s Republic of China

    Jinhai Li

Authors
  1. Suping Xu
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  2. Pingxin Wang
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  3. Jinhai Li
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  4. Xibei Yang
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  5. Xiangjian Chen
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Corresponding author

Correspondence to Xibei Yang .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Xu, S., Wang, P., Li, J., Yang, X., Chen, X. (2017). Attribute Reduction: An Ensemble Strategy. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_30

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  • DOI: https://doi.org/10.1007/978-3-319-60837-2_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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