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Application of STRIM to Datasets Generated by Partial Correspondence Hypothesis

  • Yuichi Kato
  • Tetsuro Saeki
  • Jiwi Fei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

STRIM (Statistical Test Rule Induction Method) has been proposed for an if-then rule induction method from the decision table independently of Rough Sets theory, not utilizing the notion of the approximation and the validity of the method has also been confirmed by a simulation model for data generation and verification of induced rules. However, the previous STRIM used a plain hypothesis of the complete correspondence with rules while a real-world dataset judged by human beings often seems to obey a partial correspondence hypothesis (PCH). This paper studies STRIM incorporating the PCH and improves the previous STRIM into a new version, STRIM2, of which performance and caution for use is examined by the above simulation model incorporating PCH. STRIM2 is also applied to the real-world dataset and draws results showing interesting suggestions.

Keywords

Rough sets Statistical method If-then rules 

References

  1. 1.
    Matsubayashi, T., Kato, Y., Saeki, T.: A new rule induction method from a decision table using a statistical test. In: Li, T., et al. (eds.) RSKT 2012. LNCS (LNAI), vol. 7414, pp. 81–90. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31900-6_11CrossRefGoogle Scholar
  2. 2.
    Kato, Y., Saeki, T., Mizuno, S.: Studies on the necessary data size for rule induction by STRIM. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 213–220. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41299-8_20CrossRefGoogle Scholar
  3. 3.
    Kato, Y., Saeki, T., Mizuno, S.: Considerations on rule induction procedures by STRIM and their relationship to VPRS. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS (LNAI), vol. 8537, pp. 198–208. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08729-0_19CrossRefGoogle Scholar
  4. 4.
    Kato, Y., Saeki, T., Mizuno, S.: Proposal of a statistical test rule induction method by use of the decision table. Appl. Soft Comput. 28, 160–166 (2015)CrossRefGoogle Scholar
  5. 5.
    Kato, Y., Saeki, T., Mizuno, S.: Proposal for a statistical reduct method for decision tables. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.-Z. (eds.) RSKT 2015. LNCS (LNAI), vol. 9436, pp. 140–152. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25754-9_13CrossRefGoogle Scholar
  6. 6.
    Kitazaki, Y., Saeki, T., Kato, Y.: Performance comparison to a classification problem by the second method of quantification and STRIM. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS (LNAI), vol. 9920, pp. 406–415. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47160-0_37CrossRefGoogle Scholar
  7. 7.
    Fei, J., Saeki, T., Kato, Y.: Proposal for a new reduct method for decision tables and an improved STRIM. In: Tan, Y., Takagi, H., Shi, Y. (eds.) DMBD 2017. LNCS, vol. 10387, pp. 366–378. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61845-6_37CrossRefGoogle Scholar
  8. 8.
    Kato, Y., Itsuno, T., Saeki, T.: Proposal of dominance-based rough set approach by STRIM and its applied example. In: Polkowski, L., et al. (eds.) IJCRS 2017, part I. LNCS (LNAI), vol. 10313, pp. 418–431. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60837-2_35CrossRefGoogle Scholar
  9. 9.
    Pawlak, Z.: Rough sets. Int. J. Inform. Comput. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  10. 10.
    Grzymala-Busse, J.W.: LERS – a system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, vol. 11, pp. 3–18. Springer, Dordrecht (1992).  https://doi.org/10.1007/978-94-015-7975-9_1CrossRefGoogle Scholar
  11. 11.
    Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46, 39–59 (1993)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Laboratory of Intelligent Decision Support System (IDSS). http://idss.cs.put.poznan.pl/site/139.html
  13. 13.
    Walpole, R.E., Myers, R.H., Myers, S.L., Ye, K.: Probability and Statistics for Engineers and Scientists, 8th edn, pp. 187–191. Pearson Prentice Hall, Upper Saddle River (2007)zbMATHGoogle Scholar
  14. 14.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Shimane UniversityMatsueJapan
  2. 2.Yamaguchi UniversityUbeJapan

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