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International Joint Conference on Rough Sets

IJCRS 2017: Rough Sets pp 183–199Cite as

Actionable Strategies in Three-Way Decisions with Rough Sets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10314))

Abstract

Rough set theory uses three pair-wise disjoint regions to approximate a concept. This paper adopts actionable strategies in three-way decision with rough sets. We suggest actionable rules for transferring objects from one region to another and propose a model of optimal actions based on cost-benefit analysis. Actionable strategies allow us to transfer objects from less favourable regions to a favourable region, so that we can reduce the boundary region and the negative region. We design and analyze an algorithm for searching for an optimal solution. The experimental results on a real dataset show that the algorithm has promising outcomes and objects can be effectively moved between regions.

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References

  1. Abidi, S.S.R., Hoe, K.M., Goh, A.: Analyzing data clusters: a rough sets approach to extract cluster-defining symbolic rules. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds.) IDA 2001. LNCS, vol. 2189, pp. 248–257. Springer, Heidelberg (2001). doi:10.1007/3-540-44816-0_25

    Chapter  Google Scholar 

  2. Azam, N., Yao, J.T.: Variance based determination of three-way decisions using probabilistic rough sets. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS, vol. 9920, pp. 209–218. Springer, Cham (2016). doi:10.1007/978-3-319-47160-0_19

    Chapter  Google Scholar 

  3. Chen, Z., Tang, J., Fu, A.W.-C.: Modeling and efficient mining of intentional knowledge of outliers. In: 7th International Database Engineering and Applications Symposium (IDEAS), pp. 44–53 (2003)

    Google Scholar 

  4. Dardzinska, A.: Action Rules Mining. Springer, Heidelberg (2013)

    Book  MATH  Google Scholar 

  5. Deng, X.F., Yao, Y.Y.: A multifaceted analysis of probabilistic three-way decisions. Fundam. Informaticae 132(3), 291–313 (2014)

    MathSciNet  MATH  Google Scholar 

  6. Elovici, Y., Braha, D.: A decition-theoretic approach to data mining. IEEE Trans. Syst. Man Cybern. Part A 33(1), 42–51 (2003)

    Article  Google Scholar 

  7. Gao, C., Yao, Y.Y.: An addition strategy for reduct construction. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds.) RSKT 2014. LNCS, vol. 8818, pp. 535–546. Springer, Cham (2014). doi:10.1007/978-3-319-11740-9_49

    Google Scholar 

  8. Gao, C., Yao, Y.Y.: Determining thresholds in three-way decisions with chi-square statistic. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS, vol. 9920, pp. 272–281. Springer, Cham (2016). doi:10.1007/978-3-319-47160-0_25

    Chapter  Google Scholar 

  9. Gennari, J.H., Langley, P., Fisher, D.: Models of incremental concept formation. Artif. Intell. 40, 11–61 (1989)

    Article  Google Scholar 

  10. Grzymala-Busse, J.W., Clark, P.G., Kuehnhausen, M.: Generalized probabilistic approximations of incomplete data. Int. J. Approx. Reason. 55, 180–196 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. He, Z., Xu, X., Huang, J.Z., Deng, S.: Mining class outlier: concepts, algorithms and applications in CRM. Expert Syst. Appl. 27(11), 681–697 (2004)

    Article  Google Scholar 

  12. Jonker, J.-J., Piersma, N., Van den Poel, D.: Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Syst. Appl. 27(2), 159–168 (2004)

    Article  Google Scholar 

  13. Knorr, E.M., Ng, R.T.: Finding intentional knowledge of distance-based outliers. In: VLDB 1999, pp. 211–222 (1999)

    Google Scholar 

  14. Liu, B., Hsu, W., Ma, Y.: Identifying non-actionable association rules. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 329–334 (2001)

    Google Scholar 

  15. Mishra, N., Ron, D., Swaminathan, R.: A new conceptual clustering framework. Mach. Learn. 56, 115–151 (2004)

    Article  MATH  Google Scholar 

  16. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)

    Book  MATH  Google Scholar 

  17. Pisinger, D.: Algorithms for Knapsack Problems. Ph.D. thesis, University of Copenhagen, Department of Computer Science (1995)

    Google Scholar 

  18. Ras, Z.W., Tsay, L.S.: Discovering extended action-rules (System DEAR). In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining. AINSC, vol. 22, pp. 293–300. Springer, Heidelberg (2003). doi:10.1007/978-3-540-36562-4_31

    Chapter  Google Scholar 

  19. Ras, Z.W., Wieczorkowska, A.: Action-rules: how to increase profit of a company. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS, vol. 1910, pp. 587–592. Springer, Heidelberg (2000). doi:10.1007/3-540-45372-5_70

    Chapter  Google Scholar 

  20. Shen, Y.-D., Yang, Q., Zhang, Z.: Objective-oriented utility-based association mining. In: IEEE International Conference on Data Mining (ICDM) (2002)

    Google Scholar 

  21. Silberschatz, A., Tuzihilin, A.: On subjective measures of interestingness in knowledge discovery. In: Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (KDD), pp. 275–281 (1995)

    Google Scholar 

  22. Su, P., Mao, W., Zeng, D., Zhao, H.: Mining actionable behavioral rules. Decis. Support Syst. 54(1), 142–152 (2012)

    Article  Google Scholar 

  23. Tsay, L.-S.: Interestingness measures for actionable patterns. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 277–284. Springer, Cham (2014). doi:10.1007/978-3-319-08729-0_27

    Google Scholar 

  24. Yang, Q., Cheng, H.: Mining case bases for action recommendation. In: IEEE International Conference on Data Mining (ICDM), pp. 522–529 (2002)

    Google Scholar 

  25. Yang, Q., Yin, J., Ling, C.X., Chen, T.: Postprocessing decision trees to extract actionable knowledge. In: IEEE International Conference on Data Mining (ICDM) (2003)

    Google Scholar 

  26. Yao, Y.Y.: An outline of a theory of three-way decisions. In: Yao, J.T., Yang, Y., Słowiński, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) RSCTC 2012. LNCS, vol. 7413, pp. 1–17. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32115-3_1

    Chapter  Google Scholar 

  27. Yao, Y.Y.: Probabilistic rough set approximations. Int. J. Approx. Reason. 49(2), 255–271 (2008)

    Article  MATH  Google Scholar 

  28. Yao, Y.Y.: Rough sets and three-way decisions. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.-Z. (eds.) RSKT 2015. LNCS, vol. 9436, pp. 62–73. Springer, Cham (2015). doi:10.1007/978-3-319-25754-9_6

    Chapter  Google Scholar 

  29. Yao, Y.Y.: Three-way decisions and cognitive computing. Cogn. Comput. 8(4), 543–554 (2016)

    Article  Google Scholar 

  30. Yao, Y.Y., Gao, C.: Statistical interpretations of three-way decisions. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.-Z. (eds.) RSKT 2015. LNCS, vol. 9436, pp. 309–320. Springer, Cham (2015). doi:10.1007/978-3-319-25754-9_28

    Chapter  Google Scholar 

  31. Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximating concepts. Int. J. Man-Mach. Stud. 37(6), 793–809 (1992)

    Article  Google Scholar 

  32. Zhang, H., Padmanabhan, B., Tuzhilin, A.: On the discovery of significant statistical quantitative rules. In: KDD 2004, pp. 374–383 (2004)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work is partially supported by a Discovery Grant (NSERC, Canada), Mitacs, Saskatchewan Innovation and Opportunity Graduate Scholarship, Gerhard Herzberg Fellowship, and Sampson J. Goodfellow Scholarship. The authors thank Professor Howard Hamilton and Dr. Mehdi Sadeqi for their constructive suggestions and comments.

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Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada

    Cong Gao & Yiyu Yao

Authors
  1. Cong Gao
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  2. Yiyu Yao
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Corresponding author

Correspondence to Cong Gao .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Gao, C., Yao, Y. (2017). Actionable Strategies in Three-Way Decisions with Rough Sets. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-60840-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60839-6

  • Online ISBN: 978-3-319-60840-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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