A Teacher-Cost-Sensitive Decision-Theoretic Rough Set Model

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)


Existing DTRS models use two states (success, fail) and three actions (accept, defer, reject) to describe the decision procedure. However, deferment provides a compromise instead of a solution. In this paper, we replace this action with consult which stands for consulting a teacher for correct classification. Naturally, the new action involves the cost of teacher, which is already known in many applications. Through computing the thresholds \(\alpha \) and \(\beta \) with the misclassification cost and the teacher cost from the decision system, the positive, the boundary, and the negative regions are obtained. They correspond to positive rules for acceptance, boundary rules for consulting a teacher, and negative rules for rejection, respectively. We compare the new model with the Pawlak model and previous DTRS model through an example. This study indicates a new research direction of DTRS.


Decision-theoretic rough sets Delay cost Misclassification cost Teacher cost Three-way decision 



This work is in part supported by National Science Foundation of China under Grant No. 61379089.


  1. 1.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRefGoogle Scholar
  2. 2.
    Yao, Y.Y., Wong, S.: A decision theoretic framework for approximating concepts. Int. J. Man-Mach. Stud. 37, 793–809 (1992)CrossRefGoogle Scholar
  3. 3.
    Zhang, H.R., Min, F.: Three-way recommender systems based on random forests. Knowledge-Based Systems (2015). doi: 10.1016/j.knosys.2015.06.019CrossRefGoogle Scholar
  4. 4.
    Yu, H., Wang, Y., Jiao, P.: A three-way decisions approach to density-based overlapping clustering. In: Peters, J.F., Skowron, A., Li, T., Yang, Y., Yao, J.T., Nguyen, H.S. (eds.) Transactions on Rough Sets XVIII. LNCS, vol. 8449, pp. 92–109. Springer, Heidelberg (2014) Google Scholar
  5. 5.
    Yao, Y.: The superiority of three-way decisions in probabilistic rough set models. Inf. Sci. 181, 1080–1096 (2011)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhao, H., Zhu, W.: Optimal cost-sensitive granularization based on rough sets for variable costs. Knowl.-Based Syst. 65, 72–82 (2014)CrossRefGoogle Scholar
  7. 7.
    Li, H.X., Zhou, X.Z.: Risk decision making based on decision-theoretic rough set: a three-way view decision model. Int. J. Comput. Intel. Syst. 4(1), 1–11 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Liu, D., Li, T., Liang, D.: Decision-theoretic rough sets with probabilistic distribution. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.) RSKT 2012. LNCS, vol. 7414, pp. 389–398. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  9. 9.
    Zhang, Y., Xing, H., Zou, H., Zhao, S., Wang, X.: A three-way decisions model based on constructive covering algorithm. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS, vol. 8171, pp. 346–353. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  10. 10.
    Yi, J.X., Lin, S.: A simulated annealing algorithm for learning thresholds in three-way decision-theoretic rough set model. J. Chin. Comput. Syst. 11, 2603–2606 (2013). (in chinese)Google Scholar
  11. 11.
    Turney, P.D.: Types of cost in inductive concept learning. In: Proceedings of the Workshop on Cost-Sensitive Learning at the 17th ICML, pp. 1–7 (2000)Google Scholar
  12. 12.
    Min, F., He, H.P., Qian, Y.H., Zhu, W.: Test-cost-sensitive attribute reduction. Inf. Sci. 181, 4928–4942 (2011)CrossRefGoogle Scholar
  13. 13.
    Yao, Y.Y., Zhou, B.: Micro and macro evaluation of classification rules. In: 7th IEEE International Conference on Cognitive Informatics, ICCI 2008, pp. 441–448 (2008)Google Scholar

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Authors and Affiliations

  1. 1.School of Computer ScienceSouthwest Petroleum UniversityChengduChina

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