Skip to main content
Springer Nature Link
Log in

Attribute Reduction Based on the Multi-annulus Model

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14481))

Included in the following conference series:

  • 478 Accesses

Abstract

Attribute reduction is one of the important methods in data mining preprocessing. In order to reduce the time consumption of attribute reduction, from the perspective of data distribution, the multi-annulus model is established for samples under different labels. The multi-annulus model can adaptively generate multiple annuluses with different radii according to the different data densities, and the intersection between annuluses is regarded as the boundary domain of annuluses, which introduces a new angle to solve the attribute reduction problem. The key to solving the attribute reduction of the multi-annulus model is to take the quality in the annulus as the metric criterion. Additionally, the radius ratio of the annulus and the boundary region of the annulus is used as the weight of the quality in the annulus. To obtain the change of the quality in the annulus caused by each candidate attribute added to the attribute reduction pool, the forward greedy strategy is employed. Its essence is to divide the unevenly distributed data into corresponding annuluses and reduce the preprocessing process of the data itself, so as to accelerate the process of attribute reduction. Compared with the other three mainstream attribute reduction algorithms, the final experimental results show that the proposed algorithm can greatly shorten the time of attribute reduction by introducing the multi-annulus model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen, Y., Liu, K., Song, J., Fujita, H., Yang, X., Qian, Y.: Attribute group for attribute reduction. Inf. Sci. 535, 64–80 (2020)

    Article  Google Scholar 

  2. Li, J., Yang, X., Song, X., Li, J., Wang, P., Yu, D.: Neighborhood attribute reduction: a multi-criterion approach. Int. J. Mach. Learn. Cybern. 10, 731–742 (2019)

    Article  Google Scholar 

  3. Khan, M.T., Azam, N., Khalid, S., Yao, J.T.: A three-way approach for learning rules in automatic knowledge-based topic models. Int. J. Approximate Reasoning 82, 210–226 (2017)

    Article  MathSciNet  Google Scholar 

  4. Afridi, M.K., Azam, N., Yao, J.T.: Variance based three-way clustering approaches for handling overlapping clustering. Int. J. Approximate Reasoning 118, 47–63 (2020)

    Article  MathSciNet  Google Scholar 

  5. Zhang, X., Yao, Y.: Tri-level attribute reduction in rough set theory. Expert Syst. Appl. 190, 116187 (2022)

    Article  Google Scholar 

  6. Azam, N., Zhang, Y., Yao, J.T.: Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets. Eur. J. Oper. Res. 261, 704–714 (2017)

    Article  MathSciNet  Google Scholar 

  7. Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Inf. Sci. 178, 3577–3594 (2008)

    Article  MathSciNet  Google Scholar 

  8. Wang, P., Shi, H., Yang, X., Mi, J.: Three-way k-means: integrating k-means and three-way decision. Int. J. Mach. Learn. Cybernet. 10, 2767–2777 (2019)

    Article  Google Scholar 

  9. Zhai, J., Wang, X., Zhang, S., Hou, S.: Tolerance rough fuzzy decision tree. Inf. Sci. 465, 425–438 (2018)

    Article  MathSciNet  Google Scholar 

  10. Liu, Y., et al.: Discernibility matrix based incremental feature selection on fused decision tables. Int. J. Approximate Reasoning 118, 1–26 (2020)

    Article  MathSciNet  Google Scholar 

  11. Bao Qing Hu: Three-way decisions space and three-way decisions. Inf. Sci. 281, 21–52 (2014)

    Article  MathSciNet  Google Scholar 

  12. Zhang, Y., Yao, J.T.: Gini objective functions for three-way classifications. Int. J. Approximate Reasoning 81, 103–114 (2017)

    Article  MathSciNet  Google Scholar 

  13. Li, M., Wang, G.: Approximate concept construction with three-way decisions and attribute reduction in incomplete contexts. Knowl.-Based Syst. 91, 165–178 (2016)

    Article  Google Scholar 

  14. Zhao, S., et al.: An accelerator for rule induction in fuzzy rough theory. IEEE Trans. Fuzzy Syst. 29, 3635–3649 (2021)

    Article  Google Scholar 

  15. Wang, N., Peng, Z., Cui, L.: EasiFFRA:a fast feature reduction algorithm based on neighborhood rough set. J. Comput. Res. Dev. 56, 2578–2588 (2019). (in Chinese)

    Google Scholar 

  16. Ba, J., Chen, Y., Yang, X.: Attribute partition strategy for quick searching reducts based on granular ball rough set. J. Nanjing Univ. Sci. Technol. 45, 394–400 (2021). (in Chinese)

    Google Scholar 

  17. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The cart decision tree for mining data streams. Inf. Sci. 266, 1–15 (2014)

    Article  Google Scholar 

  18. Chauhan, V.K., Dahiya, K., Sharma, A.: Problem formulations and solvers in linear SVM: a review. Artif. Intell. Rev. 52, 803–855 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 62006099, 62076111) and the Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province (No. OBDMA202104).

Author information

Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China

    Yan Liu, Jingjing Song, Taihua Xu & Jianjun Chen

  2. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, 316022, Zhejiang, China

    Taihua Xu

Authors
  1. Yan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingjing Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Taihua Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianjun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Liu .

Editor information

Editors and Affiliations

  1. IRCCS Istituto Ortopedico Galeazzi, Milano, Italy

    Andrea Campagner

  2. Ghent University, Ghent, Belgium

    Oliver Urs Lenz

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Shuyin Xia

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. AGH University of Science and Technology, Kraków, Poland

    Jarosław Wąs

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, Y., Song, J., Xu, T., Chen, J. (2023). Attribute Reduction Based on the Multi-annulus Model. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50959-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50958-2

  • Online ISBN: 978-3-031-50959-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics