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Incremental Updating Rough Approximations in Interval-valued Information Systems

  • Yingying Zhang
  • Tianrui Li
  • Chuan Luo
  • Hongmei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

Interval-valued Information System (IvIS) is a generalized model of single-valued information system, in which the attribute values of objects are all interval values instead of single values. The attribute set in IvIS is not static but rather dynamically changing over time with the collection of new information. The rough approximations may evolve accordingly, which should be updated continuously for data analysis based on rough set theory. In this paper, on the basis of the similarity-based rough set theory in IvIS, we first analyze the relationships between the original approximation sets and the updated ones. And then we propose the incremental methods for updating rough approximations when adding and removing attributes, respectively. Finally, a comparative example is used to validate the effectiveness of the proposed incremental methods.

Keywords

Interval-valued Information System Similarity Rough set Incremental updating Approximations 

Notes

Acknowledgements

This work is supported by the National Science Foundation of China (No. 61175047), NSAF (No. U1230117), the Young Software Innovation Foundation of Sichuan Province, China (No. 2014-046) and the Beijing Key Laboratory of Traffic Data Analysis and Mining (BKLTDAM2014001).

References

  1. 1.
    Inbarani, H.H., Azar, A.T., Jothi, G.: Supervised hybrid feature selection based on pso and rough sets for medical diagnosis. Comput. Methods Program. Biomed. 113(1), 175–185 (2014)CrossRefGoogle Scholar
  2. 2.
    Yao, J., Herbert, J.P.: Financial time-series analysis with rough sets. Appl. Soft Comput. 9(3), 1000–1007 (2009)CrossRefGoogle Scholar
  3. 3.
    Chen, Y., Cheng, C.: A soft-computing based rough sets classifier for classifying ipo returns in the financial markets. Appl. Soft Comput. 12(1), 462–475 (2012)CrossRefGoogle Scholar
  4. 4.
    Tseng, T.B., Jothishankar, M.C., Wu, T.T.: Quality control problem in printed circuit board manufacturingan extended rough set theory approach. J. Manufact. Syst. 23(1), 56–72 (2004)CrossRefGoogle Scholar
  5. 5.
    Wang, F.H.: On acquiring classification knowledge from noisy data based on rough set. Expert Syst. Appl. 29(1), 49–64 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bai, H.X., Ge, Y., Wang, J.F., Li, D.Y., Liao, Y.L., Zheng, X.Y.: A method for extracting rules from spatial data based on rough fuzzy sets. Knowl.-Based Syst. 57, 28–40 (2014)CrossRefGoogle Scholar
  7. 7.
    Pawlak, Z.: Information systems theoretical foundation. Inf. Syst. 3(6), 205–218 (1981)CrossRefGoogle Scholar
  8. 8.
    Chen, Z.C., Qin, K.Y.: Attribute reduction of interval-valued information system based on variable precision tolerance relation. Comput. Sci. 36(3), 163–166 (2009)Google Scholar
  9. 9.
    Leung, Y., Fischer, M.M., Wu, W.Z., Mi, J.S.: A rough set approach for the discovery of classification rules in interval-valued information systems. Int. J. Approx. Reasoning 47(2), 233–246 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Qian, Y.H., Liang, J.Y., Dang, C.Y.: Interval ordered information systems. Comput. Math. Appl. 56(8), 1994–2009 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Miao, D.Q., Zhang, N., Yue, X.D.: Knowledge reduction in interval-valued information systems. In: Proceedings of the 8th IEEE International Conference on Congitive Informatics, pp. 320–327 (2009)Google Scholar
  12. 12.
    Yang, X.B., Yu, D.J., Yang, J.Y., Wei, L.H.: Dominance-based rough set approach to incomplete interval-valued information system. Data Knowl. Eng. 68(11), 1331–1347 (2009)CrossRefGoogle Scholar
  13. 13.
    Dai, J.H., Wang, W.T., Mi, J.S.: Uncertainty measurement for interval-valued information systems. Inf. Sci. 251, 63–78 (2013)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Dai, J.H., Wang, W.T., Xu, Q., Tian, H.W.: Uncertainty measurement for interval-valued decision systems based on extended conditional entropy. Knowl.-Based Syst. 27, 443–450 (2012)CrossRefGoogle Scholar
  15. 15.
    Zhang, H.Y., Leung, Y., Zhou, L.: Variable-precision-dominance-based rough set approach to interval-valued information systems. Inf. Sci. 244, 75–91 (2013)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Du, W.S., Hu, B.Q.: Approximate distribution reducts in inconsistent interval-valued ordered decision tables. Inf. Sci. 271, 93–114 (2014)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Li, T.R., Ruan, D., Geert, W., Song, J., Xu, Y.: A rough Sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowl.-Based Syst. 20(5), 485–494 (2007)CrossRefGoogle Scholar
  18. 18.
    Luo, C., Li, T.R., Chen, H.M., Liu, D.: Incremental approaches for updating approximations in set-valued ordered information systems. Knowl.-Based Syst. 50, 218–233 (2013)CrossRefGoogle Scholar
  19. 19.
    Luo, C., Li, T.R., Chen, H.M.: Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization. Inf. Sci. 257, 210–228 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Luo, C., Li, T.R., Chen, H.M., Lu, L.X.: Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values. Inf. Sci. 299, 221–242 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Li, S.Y., Li, T.R., Liu, D.: Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set. Knowl.-Based Syst. 40, 17–26 (2013)CrossRefGoogle Scholar
  22. 22.
    Zeng, A.P., Li, T.R., Liu, D., Zhang, J.B., Chen, H.M.: A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets Syst. 258, 39–60 (2015)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Chen, H.M., Li, T.R., Luo, C., et al.: A Decision-theoretic Rough Set Approach for Dynamic Data Mining. IEEE Trans. Fuzzy Syst. (2015). doi: 10.1109/TFUZZ.2014.2387877 CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Yingying Zhang
    • 1
    • 2
  • Tianrui Li
    • 1
    • 2
  • Chuan Luo
    • 1
    • 2
  • Hongmei Chen
    • 1
    • 2
  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduChina
  2. 2.Key Lab of Cloud Computing and Intelligent TechniqueChengduChina

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