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A New Class of Necessity Measures and Fuzzy Rough Sets Based on Certainty Qualifications

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Rough Sets and Current Trends in Computing (RSCTC 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2005))

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Abstract

In this paper, we propose a new class of necessity measures which satisfy (Rl) NA(B) > 0 ⇔ ∃ε> 0; [A]1-ε ⊆ (B)ε, (R2) ∃h* ∈ (0,1); N A(B) ≥ h* ⇔ AB and (R3) N A(B) = 1 ⇔ (A)0 ⊆ [B]1. It is shown that such a necessity measure is designed easily by level cut conditioning approach. A simple example of such a necessity measure is given. The proposed necessity measure is applied to fuzzy rough set based on certainty qualifications. It is demonstrated that the proposed necessity measure gives better upper and lower approximations of a fuzzy set than necessity measures defined by S-, R- and reciprocal R-implications.

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References

  1. Dubois, D., Prade, H.: Putting Rough Sets and Fuzzy Sets Together. In: Slowinski, R.(ed.), Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht (1992) 203–232

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  2. Fodor, J., Roubens, M.: Fuzzy Preference Modeling and Multicriteria Decision Making. Kluwer Academic Publishers, Dordrecht (1994)

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  3. Inuiguchi, M., Tanino, T.: Level Cut Conditioning Approach to the Necessity Measure Specification. In: Zhong, N., Skowron, A., Ohsuga, S.(eds.): New Directions in Rough Sets, Data Mining, and Granular-Soft Computing: Proceedings of 7th International Workshop, RSFDGrC’99, Springer, Berlin (1999) 193–202

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  4. Inuiguchi, M., Tanino, T.: Fuzzy Rough Sets Based on Certainty Qualifications. Proceedings of AFSS 2000 (2000) 433–438

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© 2001 Springer-Verlag Berlin Heidelberg

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Inuiguchi, M., Tanino, T. (2001). A New Class of Necessity Measures and Fuzzy Rough Sets Based on Certainty Qualifications. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_31

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  • DOI: https://doi.org/10.1007/3-540-45554-X_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43074-2

  • Online ISBN: 978-3-540-45554-7

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