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Rough Sets Based on Possible Indiscernibility Relations in Incomplete Information Tables with Continuous Values

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11499))

Abstract

Rough sets under incomplete information with continuous domains are examined on the basis of possible world semantics. We show an approach under possible indiscernibility relations, although the traditional approaches are done under possible tables. This is because the number of possible indiscernibility relations is finite, even if the number of possible tables is infinite. First, lower and upper approximations are described using the indiscernibility relation on an attribute in a complete information table. Second, these are addressed in an incomplete information table under possible world semantics. Two types of indiscernibility relations; namely, certain and possible ones, are obtained on an attribute in an information table. The actual indiscernibility relation is one of possible ones. The family of indiscernibility relations is a lattice for inclusion. The minimal element is the certain indiscernibility relation while the maximal one is the maximal possible indiscernibility relation. By using certain and possible indiscernibility relations, we obtain four types of approximations: certain lower, certain upper, possible lower, and possible upper approximations. The approach based on possible world semantics gives the same approximations as ones obtained from our extended approach, which is proposed in the previous work directly using indiscernibility relations.

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Notes

  1. 1.

    For the sake of simplicity and space limitation, We describe the case of an attribute, although our approach can be easily extended to the case of more than one attribute.

  2. 2.

    Hu and Yao also say that approximations are described by using an interval set in information tables with incomplete information [5].

References

  1. Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley Publishing Company, Reading (1995)

    MATH  Google Scholar 

  2. Bosc, P., Duval, L., Pivert, O.: An initial approach to the evaluation of possibilistic queries addressed to possibilistic databases. Fuzzy Sets Syst. 140, 151–166 (2003)

    Article  MathSciNet  Google Scholar 

  3. Grahne, G. (ed.): The Problem of Incomplete Information in Relational Databases. LNCS, vol. 554. Springer, Heidelberg (1991). https://doi.org/10.1007/3-540-54919-6

    Book  MATH  Google Scholar 

  4. Grzymala-Busse, J.W.: Mining numerical data – a rough set approach. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets XI. LNCS, vol. 5946, pp. 1–13. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11479-3_1

    Chapter  Google Scholar 

  5. Hu, M.J., Yao, Y.Y.: Rough set approximations in an incomplete information table. In: Polkowski, L., et al. (eds.) IJCRS 2017, Part II. LNCS (LNAI), vol. 10314, pp. 200–215. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60840-2_14

    Chapter  Google Scholar 

  6. Imielinski, T., Lipski, W.: Incomplete information in relational databases. J. ACM 31, 761–791 (1984)

    Article  MathSciNet  Google Scholar 

  7. Jing, S., She, K., Ali, S.: A universal neighborhood rough sets model for knowledge discovering from incomplete heterogeneous data. Expert Syst. 30(1), 89–96 (2013). https://doi.org/10.1111/j.1468-0394.2012.00633_x

    Article  Google Scholar 

  8. Kryszkiewicz, M.: Rules in incomplete information systems. Inf. Sci. 113, 271–292 (1999)

    Article  MathSciNet  Google Scholar 

  9. Lipski, W.: On semantics issues connected with incomplete information databases. ACM Trans. Database Syst. 4, 262–296 (1979)

    Article  Google Scholar 

  10. Lipski, W.: On databases with incomplete information. J. ACM 28, 41–70 (1981)

    Article  MathSciNet  Google Scholar 

  11. Lin, T.Y.: Neighborhood systems: a qualitative theory for fuzzy and rough sets. In: Wang, P. (ed.) Advances in Machine Intelligence and Soft Computing, vol. IV, pp. 132–155. Duke University (1997). https://doi.org/10.1007/11548669_34

    Google Scholar 

  12. Nakata, M., Sakai, H.: Applying rough sets to information tables containing missing values. In: Proceedings of 39th International Symposium on Multiple-Valued Logic, pp. 286–291. IEEE Press (2009). https://doi.org/10.1109/ISMVL.2009.1

  13. Nakata, M., Sakai, H.: Twofold rough approximations under incomplete information. Int. J. Gen. Syst. 42, 546–571 (2013). https://doi.org/10.1080/17451000.2013.798898

    Article  MathSciNet  MATH  Google Scholar 

  14. Nakata, M., Sakai, H.: Describing rough approximations by indiscernibility relations in information tables with incomplete information. In: Carvalho, J.P., et al. (eds.) IPMU 2016, Part II. CCIS, vol. 611, pp. 355–366. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40581-0_29

    Chapter  Google Scholar 

  15. Nakata, M., Sakai, H., Hara, K.: Rules induced from rough sets in information tables with continuous values. In: Medina, J., et al. (eds.) IPMU 2018, Part II. CCIS, vol. 854, pp. 490–502. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91476-3_41

    Chapter  Google Scholar 

  16. Nakata, M., Sakai, H., Hara, K.: Rule induction based on indiscernible classes from rough sets in information tables with continuous values. In: Nguyen, H.S., Ha, Q.-T., Li, T., Przybyła-Kasperek, M. (eds.) IJCRS 2018. LNCS (LNAI), vol. 11103, pp. 323–336. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99368-3_25

    Chapter  Google Scholar 

  17. Paredaens, J., De Bra, P., Gyssens, M., Van Gucht, D.: The Structure of the Relational Database Model. Springer, Heidelberg (1989). https://doi.org/10.1007/978-3-642-69956-6

    Book  MATH  Google Scholar 

  18. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991). https://doi.org/10.1007/978-94-011-3534-4

    Book  MATH  Google Scholar 

  19. Stefanowski, J., Tsoukiàs, A.: Incomplete information tables and rough classification. Comput. Intell. 17, 545–566 (2001)

    Article  Google Scholar 

  20. Yang, X., Zhang, M., Dou, H., Yang, Y.: Neighborhood systems-based rough sets in incomplete information system. Inf. Sci. 24, 858–867 (2011). https://doi.org/10.1016/j.knosys.2011.03.007

    Article  Google Scholar 

  21. Zenga, A., Lia, T., Liuc, D., Zhanga, J., Chena, H.: A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets Syst. 258, 39–60 (2015). https://doi.org/10.1016/j.fss.2014.08.014

    Article  MathSciNet  Google Scholar 

  22. Zhao, B., Chen, X., Zeng, Q.: Incomplete hybrid attributes reduction based on neighborhood granulation and approximation. In: 2009 International Conference on Mechatronics and Automation, pp. 2066–2071. IEEE Press (2009)

    Google Scholar 

  23. Zimányi, E., Pirotte, A.: Imperfect information in relational databases. In: Motro, A., Smets, P. (eds.) Uncertainty Management in Information Systems: From Needs to Solutions, pp. 35–87. Kluwer Academic Publishers (1997)

    Google Scholar 

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Acknowledgment

The authors wish to thank the anonymous reviewers for their valuable comments.

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Correspondence to Michinori Nakata .

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Nakata, M., Sakai, H., Hara, K. (2019). Rough Sets Based on Possible Indiscernibility Relations in Incomplete Information Tables with Continuous Values. In: Mihálydeák, T., et al. Rough Sets. IJCRS 2019. Lecture Notes in Computer Science(), vol 11499. Springer, Cham. https://doi.org/10.1007/978-3-030-22815-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-22815-6_13

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