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A New Method to Calculate the Appropriateness Measures of Label Expressions in Uncertainty Model

DOI: 10.4236/oalib.1102399, PP. 1-7

Subject Areas: Mathematical Analysis

Keywords: Epistemic Vagueness, Label Semantics, Random Sets, Appropriateness Measure, Disjunctive Normal Form

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Abstract

The appropriateness measure of label expression is a basal concept in uncertainty modelling based on label semantics theory for dealing with vague concepts. In the paper, the concept of disjunctive normal forms is presented. It is proved that each label expression is semantic equivalent to a disjunctive normal form. Further, a new method of calculating the appropriateness measures of label expressions is provided.

Cite this paper

Liu, Y. and Zhang, X. (2016). A New Method to Calculate the Appropriateness Measures of Label Expressions in Uncertainty Model. Open Access Library Journal, 3, e2399. doi: http://dx.doi.org/10.4236/oalib.1102399.

References

[1]  Chen, L., Mu, Z.C. and Nan, B.F. (2015) Semantic Image Segmentation Based on Hierarchical Conditional Random Field Model. Journal of Computational Information Systems, 11, 527-534.
[2]  Nilsson, N. (1986) Probability Logic. Artificial Intelligence, 28, 71-78.
http://dx.doi.org/10.1016/0004-3702(86)90031-7
[3]  Zadeh, L.A. (1973) Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man and Cybernetics, 3, 28.
http://dx.doi.org/10.1109/TSMC.1973.5408575
[4]  Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353.
http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[5]  Zadeh, L.A. (1975) Fuzzy Logic and Approximate Reasoning. Synthese, 30, 407-428.
http://dx.doi.org/10.1007/BF00485052
[6]  Lukasiewicz, J. and Tarski, A. (1930) Untersuchungen über den Aussagenkalkül. Comptes Rendus des Séances de la Sociélé des Scierices et des Lettres des Varsovie Classe III, 23, 30-50.
[7]  Wang, G. and Zhou, H. (2009) Introduction to Mathematical Logic and Resolution Principle. 2nd Edition, Science Press Beijing, and Alpha Science International Limited, Oxford.
[8]  Wang, G. (1997) A Formal Deduction System of Fuzzy Propositional Calculation. Science in China Series E-Informa- tion Sciences, 42, 1041-1044.
[9]  Doubois, D. and Prade, H. (2001) Possibility Theory, Probability Theory and Multiple-Valued Logic. Annals of Mathematics and Artificial Intelligence, 32, 35-66.
http://dx.doi.org/10.1023/A:1016740830286
[10]  Hajek, P. (1998) Metamathematics of Fuzzy Logic. Kluwer Academic Publishers, London, 89-120.
http://dx.doi.org/10.1007/978-94-011-5300-3
[11]  Esteva, F. and Godo, L. (2001) Monoidal t-Norm Based Logic: Towards Logic for Left-Continuous t-Norms. Fuzzy Sets and Systems, 124, 271-288.
http://dx.doi.org/10.1016/S0165-0114(01)00098-7
[12]  Dubois, D. and Prade, H. (1988) An Introduction to Possibility and Fuzzy Logics. In: Smets, P., Mamdani, A., Dubois, D. and Prade, H., Eds., Non-Standard Logics for Automated Reasoning, Academic Press, London, 742-755.
[13]  Dubois, D. and Prade, H. (1990) Measuring Properties of Fuzzy Sets: A General Technique and Its Use in Fuzzy Query Evaluation. Fuzzy Sets and Systems, 38, 137-152.
http://dx.doi.org/10.1016/0165-0114(90)90146-W
[14]  Dubois, D. and Prade, H. (1997) The Three Semantics of Fuzzy Sets. Fuzzy Sets and Systems, 90, 141-150.
http://dx.doi.org/10.1016/S0165-0114(97)00080-8
[15]  Dubois, D., Godo, L., Prade, H. and Esteva, F. (2005) An Information-Based Discussion of Vagueness. In: Cohen, H. and Lefebre, C., Eds., Handbook of Categorization in Cognitive Science, Elsevier Science, Amsterdam, 891-909.
http://dx.doi.org/10.1016/B978-008044612-7/50095-0
[16]  Li, L. and Zhang, J. (2010) Attribute Reduction in Fuzzy Concept Lattices Based on the T Implication Original Research Article Pages. Knowledge-Based Systems, 23, 497-503.
http://dx.doi.org/10.1016/j.knosys.2010.03.006
[17]  Miller, S. and John, R. (2010) An Interval Type-2 Fuzzy Multiple Echelon Supply Chain Model Original Research Article Pages. Knowledge-Based Systems, 23, 363-368.
http://dx.doi.org/10.1016/j.knosys.2009.11.016
[18]  Buckley, J., Siler, W. and Tucker, D. (1986) A Fuzzy Expert System. Fuzzy Sets and Systems, 20, 1-16.
http://dx.doi.org/10.1016/S0165-0114(86)80027-6
[19]  Egemen, A. and Telatar, Y.Z. (2010) Note-against-Note Two-Voice Counterpoint by Means of Fuzzy Logic Original Research Article. Knowledge-Based Systems, 23, 256-266.
http://dx.doi.org/10.1016/j.knosys.2010.01.007
[20]  Elkan, C. (1994) The Paradoxical Success of Fuzzy Logic. IEEE Expert, 9, 3-8.
http://dx.doi.org/10.1109/64.336150
[21]  Elkan, C. (1994) The Paradoxical Controversy over Fuzzy Logic. IEEE Expert, 9, 47-49.
http://dx.doi.org/10.1109/64.336150
[22]  Watkins, F.A. (1995) False Controversy: Fuzzy and Non-Fuzzy Faux Pas. IEEE Expert, 10, 4-5.
[23]  Zhang, X. (2011) Duality and Pseudo Duality of Dual Disjunctive Normal Forms. Knowledge-Based Systems, 24, 1033-1036.
http://dx.doi.org/10.1016/j.knosys.2011.04.017
[24]  Zadeh, L.A. (1975) The Concept of Linguistic Variable and Its Application to Approximate Reasoning, Part 2. Information Sciences, 8, 301-357.
http://dx.doi.org/10.1016/0020-0255(75)90046-8
[25]  Flaminio, T. and Godo, L. (2007) A Logic for Reasoning about the Probability of Fuzzy Events. Fuzzy Sets and Systems, 158, 625-638.
http://dx.doi.org/10.1016/j.fss.2006.11.008
[26]  Coletti, G. and Scozzafava, R. (2002) Probability Logic in a Coherent Setting. Kluwer Academic Publishers, London.
http://dx.doi.org/10.1007/978-94-010-0474-9
[27]  Fine, K. (1975) Vagueness, Truth and Logic. Synthese, 30, 265-300.
http://dx.doi.org/10.1007/BF00485047
[28]  Hailperin, T. (1996) Sentential Probability Logic. Associated University Presses, London.
[29]  Flaminioa, T. and Godo, L. (2007) A Logic for Reasoning about the Probability of Fuzzy Events. Fuzzy Sets and Systems, 158, 625-638.
http://dx.doi.org/10.1016/j.fss.2006.11.008
[30]  Lawry, J. (2004) A Framework for Linguistic Modeling. Artificial Intelligence, 155, 1-39.
http://dx.doi.org/10.1016/j.artint.2003.10.001
[31]  Lawry, J. (2006) Modelling and Reasoning with Vague Concepts. Springer, Berlin.
[32]  Lawry, J. (2008) Appropriateness Measures: An Uncertainty Model for Vague Concepts. Synthese, 161, 255-269.
http://dx.doi.org/10.1007/s11229-007-9158-9
[33]  Lawry, J. (2008) An Overview of Computing with Words Using Label Semantics. In: Bustince, H., Herrera, F. and Montero, J., Eds., Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, Springer, Berlin, 65-87.
http://dx.doi.org/10.1007/978-3-540-73723-0_4
[34]  Lawry, J. and Tang, Y. (2009) Uncertainty Modelling for Vague Concepts: A Prototype Theory Approach. Artificial Intelligence, 173, 1539-1558.
http://dx.doi.org/10.1016/j.artint.2009.07.006
[35]  Tang, Y. and Zheng, J. (2006) Linguistic Modelling Based on Semantic Similarity Relation amongst Linguistic Labels. Fuzzy Sets and Systems, 157, 1662-1673.
http://dx.doi.org/10.1016/j.fss.2006.02.014

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