Summary
We present appropriate strategies and ways to represent knowledge for classification problems. These problems have noticeable characteristics, like plausible reasoning, a deep gap between data and solutions, noisy and unreliable data and they need a suitable expert system architecture. After a survey of different frameworks to represent inexact knowledge, we describe an object classification system, developed at INRIA, and based on fuzzy pattern matching techniques. We finally show how adequate control strategies may handle incomplete and contradictory data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aikins JS (1980) Prototypes and Production Rules: A Knowledge Representation for Computer Consultations. PhD thesis, Computer Science Department, Stanford University
Buisson JC, Farreny, H, Prade H (1986) Dealing with Imprecision and Uncertainty in the Expert System Diabeto-Ill, in: Proc. 2e Colloque International d’Intelligence Artificielle de Marseille. Marseille, France
Cayrol M, Farreny H, Prade H. (1982) Fuzzy Pattern Matching. Kybernetes 11
Chailloux J, Devin M, Hullot JM. (1984) LeLisp, a Portable and Efficient Lisp System, in: Proceedings of ACM Conference on Lisp and Functional Programming. Austin, Texas
Clancey WJ (1984) Classification Problem Solving, in: Proceeding of the National Conference on AI. Austin, Texas
Dubois D, Prade H (1985) Théorie des Possibilités. Applications à la Représentation des Connaissances en Informatique. Masson, Paris, France
Dubois D, Prade H, Testemale C (1986) Weighted Fuzzy Pattern Matching. Journée Nationale sur les Ensembles Flous, Toulouse, France
Ganascia JG (1984) Reasoning and Results in Expert Systems: Main Differences between Diagnostic Systems and Problem Solvers, in: Proceedings of ECAI84, Pisa, Italy
Gandelin M-H (1985) Etude de Faisabilité d’un Système Expert Appliqué à l’Identification Automatique d’Organismes Planctoniques. Diplôme d’Etudes Approfondies, Université de Nice
Granger C (1985) Reconnaissance d’objets par Mise en Correspondance en Vision par Ordinateur. Thèse de Doctorat, Université de Nice
Shafer G (1976) A Mathematical Theory of Evidence. Princeton University Press, Princeton
Shortliffe EH, Buchanan BG (1975) A Model of Inexact Reasoning in Medicine. Mathematical Biosciences 23
Sonia G, Vialettes B, San Marco JL (1983) Protis, a Fuzzy Deduction-rule System: Application to the Treatment of Diabetes, in: Proc. MEDINFO83, Amsterdam
Thonnat M (1985) Automatic Morphological Description of Galaxies and Classification by an Expert System. Rapport de recherche INRIA
Zadeh LA (1978) Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems 1
Zadeh LA (1979) A Theory of Approximate Reasoning, in: Hayes JE (ed.) Machine Intelligence. Elsevier
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1988 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Granger, C. (1988). Fuzzy Reasoning for Classification: An Expert System Approach. In: Gaul, W., Schader, M. (eds) Data, Expert Knowledge and Decisions. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-73489-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-73489-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-73491-5
Online ISBN: 978-3-642-73489-2
eBook Packages: Springer Book Archive