Abstract
The rough set theory was proved of its effectiveness in dealing with the imprecise and ambiguous information. Dominance-based Rough Set Approach (DRSA), as one of the extensions, is effective and fundamentally important for Multiple Criteria Decision Analysis (MCDA). However, most of existing DRSA models cannot directly examine uncertain information within rough boundary regions, which might miss the significant knowledge for decision support. In this paper, we propose a new believe factor in terms of an intuitionistic fuzzy value as foundation, further to induce a kind of new uncertain rule, called believable rules, for better performance in decision-making. We provide an example to demonstrate the effectiveness of the proposed approach in multicriteria sorting and also a comparison with existing representative DRSA models.
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References
Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets and Systems 20, 87–96 (1986)
Blaszczynski, J., Greco, S., Slowinski, R.: Multi-criteria classification: a new scheme for application of dominance-based decision rules. European Journal of Operational Research 181, 1030–1044 (2007)
Chai, J.Y., Liu, J.N.K.: Class-based rough approximation with dominance principle. In: Proceedings of IEEE International Conference on Granular Computing (GrC), pp. 77–82 (2011)
Chai, J.Y., Liu, J.N.K., Xu, Z.S.: A new rule-based SIR approach to supplier selection under intuitionistic fuzzy environments. International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems 20(3) (2012)
Chang, B., Hung, H.F.: A study of using RST to create the supplier selection model and decision-making rules. Expert Systems with Applications 37, 8284–8295 (2010)
Cyran, K.A.: Quasi Dominance Rough Set Approach in Testing for Traces of Natural Selection at Molecular Level. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AISC, vol. 59, pp. 163–172. Springer, Heidelberg (2009)
Greco, S., Matarazzo, B., Słowiński, R., Stefanowski, J.: Variable Consistency Model of Dominance-Based Rough Sets Approach. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 170–181. Springer, Heidelberg (2001)
Greco, S., Matarazzo, B., Slowinski, R.: Decision rule approach. In: Figueira, J., Greco, S., Ehrgott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 507–561. Springer, Berlin (2005)
Inuiguchi, M., Yoshioka, Y., Kusunoki, Y.: Variable-precision dominance-based rough set approach and attribute reduction. International Journal of Approximate Reasoning 50, 1199–1214 (2009)
Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems 114, 505–518 (2000)
Xu, Z.S.: Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems 15, 1179–1187 (2007)
Xu, Z.S., Cai, X.Q.: Recent advances in intuitionistic fuzzy information aggregation. Fuzzy Optimization and Decision Making 9(4), 359–381 (2010)
Zhang, Z.W., Shi, Y., Gao, G.X.: A rough set-based multicriteria criteria linear programming approach for the medical diagnosis and prognosis. Expert Systems with Applications 36(5), 8932–8937 (2009)
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Chai, J., Liu, J.N.K., Li, A. (2012). A New Intuitionistic Fuzzy Rough Set Approach for Decision Support. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_10
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DOI: https://doi.org/10.1007/978-3-642-31900-6_10
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