An Accelerator of Feature Selection Applying a General Fuzzy Rough Model

  • Peng Ni
  • Suyun ZhaoEmail author
  • Hong Chen
  • Cuiping Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)


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.


Feature selection Fuzzy rough techniques Accelerator Fuzzy positive region 



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).


  1. 1.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. JMLR. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  2. 2.
    Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the 9th International Conference on Machine Learning, pp. 249–256. Morgan Kaufmann, Los Altos (1992)Google Scholar
  3. 3.
    Peng, H.C., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  4. 4.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston (1991)CrossRefzbMATHGoogle Scholar
  5. 5.
    Pawlak, Z., Grzymala-Busse, J.W., Slowiski, R., Ziako, W.: Rough sets. Commun. ACM 38(11), 89–95 (1995)CrossRefGoogle Scholar
  6. 6.
    Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17, 109–137 (1990)CrossRefzbMATHGoogle Scholar
  7. 7.
    Wang, X.Z., Tang, E.C.C., Zhao, S.Y., Chen, D.G.: Learning fuzzy rules from fuzzy samples based on rough set techniques. Inf. Sci. 177, 4493–4514 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Hu, Q., Yu, D.R., Xie, Z.X.: Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn. Lett. 27(5), 414–423 (2006)CrossRefGoogle Scholar
  9. 9.
    Qian, Y.H., Liang, J.Y., Pedrycz, W., Dang, C.Y.: Positive approximation: an accelerator for feature reduction in rough set theory. Artif. Intell. 174(9), 597–618 (2010)CrossRefzbMATHGoogle Scholar
  10. 10.
    Qian, Y.H., Wang, Q., Cheng, H.H., Liang, J.Y., Dang, C.Y.: Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst. 258(C), 1–78 (2015)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefzbMATHGoogle Scholar
  12. 12.
    Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17(2–3), 191–209 (1990)CrossRefzbMATHGoogle Scholar
  13. 13.
    Tsang, E.C.C., Chen, D.G., Yeung, D.S., Wang, X.Z., Lee, J.W.T.: Attributes reduction using fuzzy rough sets. IEEE Trans. Fuzzy Syst. 16(5), 1130–1141 (2008)CrossRefGoogle Scholar
  14. 14.
    Yeung, D.S., Chen, D.G., Tsang, E.C.C., Lee, J.W.T., Wang, X.Z.: On the generalization of fuzzy rough sets. IEEE Trans. Fuzzy Syst. 13, 343–361 (2005)CrossRefGoogle Scholar
  15. 15.
    Hu, Q.H., Zhang, L., An, S., Zhang, D., Yu, D.R.: On robust fuzzy rough set models. IEEE Trans. Fuzzy Syst. 20(4), 636–651 (2012)CrossRefGoogle Scholar
  16. 16.
    Yao, Y.Y., Zhao, Y., Wang, J.: On reduct construction algorithms. Trans. Comput. Sci. 2, 100–117 (2008)zbMATHGoogle Scholar
  17. 17.
    Coomans, D., Massart, D.L.: Alternative k-nearest neighbour rules in supervised pattern recognition: part 1. K-Nearest neighbour classification by using alternative voting rules. Analytica Chimica Acta 136, 15–27 (1982)CrossRefGoogle Scholar
  18. 18.
    Kryszkiewicz, M., Lasek, P.: FUN: fast discovery of minimal sets of attributes functionally determining a decision attribute. Trans. Rough Sets 9, 76–95 (2008)Google Scholar
  19. 19.
    Zhao, S.Y., Chen, H., Li, C.P., Zhai, M.Y., Du, X.Y.: RFRR: robust fuzzy rough reduction. IEEE Trans. Fuzzy Syst. 21(5), 825–841 (2013)CrossRefGoogle Scholar
  20. 20.
    Bhatt, R.B., Gopal, M.: On fuzzy rough sets approach to feature selection. Pattern Recogn. Lett. 26(7), 965–975 (2005)CrossRefGoogle Scholar
  21. 21.
    Chen, D.G., Tsang, E.C.C., Zhao, S.Y.: Attributes reduction with fuzzy rough sets. In: IEEE International Conference on Systems, Man, and Cybernetics, Vol. 1, pp. 486–491 (2007)Google Scholar
  22. 22.
    Zhao, S.Y., Wang, X.Z., Chen, D.G., Tsang, E.C.C.: Nested structure in parameterized rough reduction. Inf. Sci. 248, 130–150 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recogn. Lett. 24(6), 833–849 (2003)CrossRefzbMATHGoogle Scholar
  24. 24.
    Chen, D.G., Yang, Y.Y.: Attribute reduction for heterogeneous data based on the combination of classical and fuzzy rough set models. IEEE Trans. Fuzzy Syst. 22(5), 1325–1334 (2014)CrossRefGoogle Scholar
  25. 25.
    Chen, D.G., Zhao, S.Y.: Local reduction of decision system with fuzzy rough sets. Fuzzy Sets Syst. 161(13), 1871–1883 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peng Ni
    • 1
    • 2
  • Suyun Zhao
    • 1
    • 2
    Email author
  • Hong Chen
    • 1
    • 2
  • Cuiping Li
    • 1
    • 2
  1. 1.Key Lab of Data Engineering and Knowledge Engineering of MOERenmin University of ChinaBeijingPeople’s Republic of China
  2. 2.Information of SchoolRenmin University of ChinaBeijingPeople’s Republic of China

Personalised recommendations