Abstract
The fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared to requirements (which may be fuzzy) in a pattern matching process. This paper restates the basic principles and extends them to situations where (sub)patterns are only required to be satisfied up to a given tolerance (which may be fuzzy), or where the different subparts of a compound pattern may have various levels of importance. Both cases correspond to a relaxation of elementary patterns, which can be expressed by a fuzzy relation modelling an approximate equality or an uncertain strict equality respectively. We also study the more sophisticated case where some elementary patterns have not to be satisfied with the highest priority provided that weaker requirements remain satisfied. The fuzzy pattern matching technique applies in a variety of problems including the evaluation of soft queries with respect to a fuzzy database, the evaluation of the fuzzy condition parts of rules in approximate reasoning, or the evaluation of the membership of an ill-known object to a flexible class in classification problems.
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References
Bensana E., Bel G., Dubois D. OPAL: A multi-knowledge-based system for industrial job-shop scheduling.Int. J. Prod. Research, 26 (5), 1988, 795–819.
Cayrol M., Farreny H., Prade H. Possibility and necessity in a pattern-matching process.Proc. of the 9th Inter. Congress on Cybernetics, Namur, Belgium, Sept. 1980, 53–65.
Cayrol M., Farreny H., Prade H. Fuzzy pattern matching. Kybemetes, 11, 1982, 103–116.
Dubois D., Prade H. (with the collaboration of Farreny H., Martin-Clouaire R., Testemale C.) Possibility Theory — An Approach to Computerized Processing of Uncertainty. Plenum Press, New York, 1988.
Dubois D., Prade H., Rossazza J.P. Vagueness, typicality, and uncertainty in class hierarchies.Int. J. of Intelligent Systems, 6, 1991, 167–183.
Dubois D., Prade H., Testemale C. Weighted fuzzy pattern matching.Fuzzy Sets and Systems, 28, 1988, 313–331.
Farreny H., Prade H. On the problem of identifying an object in a robotics scene from a verbal imprecise description. In: Advanced Software in Robotics ( A. Danthine, M. Géradin, eds.), North-Holland, Amsterdam, 1984, 343–351.
Granger C. An application of possibility theory to object recognition.Fuzzy Sets and Systems,28, 1988, 351–362.
Lebailly J., Martin-Clouaire R., Prade H. Use of fuzzy logic in a rule-based system in petroleum geology. In: Approximate Reasoning in Intelligent Systems, Decision and Control (E. Sanchez, L.A. Zadeh, eds. ), Pergamon Press, 1986, 125–144.
Pedrycz W., Bortolan G., Degani R. Classification of electrocardiographic signals: A fuzzy pattern matching approach. Artificial Intelligence in Medicine, 3, 1991, 211–226.
Prade H. A two-layer fuzzy pattern matching procedure for the evaluation of conditions involving vague quantifiers.J. of Intelligent and Robotics Systems, 3, 1990, 93–101.
Prade H., Testemale C. Generalizing data base relational algebra for a treatment of incomplete or uncertain information and vague queries.Information Sciences, 34, 1984, 115–143.
Roy B., Bouyssou D. Aide Multicritère à la Décision: Méthodes et Cas. Economica, Paris, 1993.
Salotti S. Représentation centrée objet et filtrage flou pour raisonner par analogie: Le système FLORAN. Actes du 7ème Congrès Reconnaissance des Formes et Intelligence Artificielle, Paris, Nov. 29-Dec. 1, 1989, AFCET Publ., 1695–1707.
Sanchez E. Fuzzy logic and neural networks in Artificial Intelligence and Pattern Recognition. SPI, 1569, Stochastic and Neural Methods in Signal Processing, Image Processing and Computer Vision, 1991, 474–483.
Vaina L.M., Jaulent M.C. Object structure and action requirements: A compatibility model for functional recognition.Int. J. of Intelligent Systems, 6, 1991, 313–336.
Vandenberghe R., Van Schooten A., De Caluwe R., Kerre E.E. Some practical aspects of fuzzy database techniques: An example. Information Systems, 14 (6), 1989, 465–472.
Yager R.R. Robot planning with fuzzy sets. Robotica, 1, 1983, 41–50.
Zadeh L.A. Fuzzy sets as a basis for a theory of possibility.Fuzzy Sets and Systems, 1, 1978, 3–28.
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© 1995 Springer-Verlag Berlin Heidelberg
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Dubois, D., Prade, H. (1995). Tolerant Fuzzy Pattern Matching: An Introduction. In: Bosc, P., Kacprzyk, J. (eds) Fuzziness in Database Management Systems. Studies in Fuzziness, vol 5. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1897-0_3
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DOI: https://doi.org/10.1007/978-3-7908-1897-0_3
Publisher Name: Physica, Heidelberg
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