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Fuzzy Classification: An Overview

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Fuzzy-Systems in Computer Science

Part of the book series: Artificial Intelligence / Künstliche Intelligenz ((CI))

Abstract

Fuzzy classification, fuzzy diagnosis, and fuzzy data analysis are — besides fuzzy control — the most important application areas of fuzzy logic. In this chapter four practical tasks are presented which can roughly be characterized as technical classification and diagnosis, fuzzy data analysis in chemical model creation, medical object recognition, and decision making support by a life insurance. Neural networks and analytical methods of classical statistics try to find explicitely a classifying function with the help of a sample. The development of knowledge-based systems has stimulated modern constructive approaches like IF-THEN-rules and causal networks. In order to deal with vague observations, vague relationships between features, and/or non-crip classification, these analytical and constructive methods were transfered from crisp numbers to fuzzy sets. The contributions of fuzzy sets to the four applications of this chapter are presented. Some general remarks on the applicability and limitations of fuzzy classification conclude this short introduction to fuzzy classification.

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References

  1. P. Arabshahi, J. J. Choi, J. Marks & T. P. Candell: “Fuzzy Control of Backpropagation”. Proceed. IEEE Intern. Conf. on Fuzzy Systems, San Diego, 1992.

    Google Scholar 

  2. S. K. Andersen, K. G. Olesen, F. V. Jensen & F. Jensen: “HUGIN — a Shell for Building Bayesian Belief Universes for Expert Systems”. Proceed. 11th Int. Conf. Artif. Intell., Detroit, 1989.

    Google Scholar 

  3. J. C. Bezdek: “Pattern Recognition with Fuzzy Objective Function Algorithm”. Plenum Press, New York, 1981.

    Google Scholar 

  4. S. Bocklisch & N. Bitterlich: “Fuzzy Pattern Classification — Methodology and Applications”. In: R. Kruse, J. Gebhardt & R. Palm (eds.): “Fuzzy Systems in Computer Science”. Vieweg Verlag, Braunschweig, 1994.

    Google Scholar 

  5. J. C. Bezdek & J. D. Harris: “Convex Decompositions of Fuzzy Partitions”. In: D. Dubois, H. Prade, R. R. Yager (eds.): “Fuzzy Sets for Intelligent Systems”. Morgan Kaufmann Publ., San Mateo, 1993, pp. 617–628.

    Google Scholar 

  6. H. Bandemer & W. Näther: “Fuzzy Data Analysis”. Kluwer Pubi., Dordrecht, 1992.

    Book  MATH  Google Scholar 

  7. B. G. Buchanan & E. Shortliffe (eds.): “Rule-Based Expert Systems: The MYCIN Experiments on the Stanford Heuristic Programming Project”. Addison Wesley, New York, 1984.

    Google Scholar 

  8. K. Brahim & A. Zell: “ANFIS-SNSS: Adaptive Network Fuzzy Inference System in the ‘Stuttgarter Neuronale Netze Simulator’” . In: R. Kruse, J. Gebhardt & R. Palm (eds.): “Fuzzy Systems in Computer Science”. Vieweg Verlag, Braunschweig, 1994

    Google Scholar 

  9. A. Celmins: “Least Squares Model Fitting to Fuzzy Vector Data”. Fuzzy Sets and Systems 22 (1987), pp. 245–269.

    Article  MathSciNet  Google Scholar 

  10. W. Clancey: “Heuristic Classification”. Artif. Intell. 27 (1985), pp. 289–350.

    Article  Google Scholar 

  11. P.-R. Chang & C. C. Tai: “Model-Reference Neural Color Correction for HDTV-Systems based on Fuzzy Information Criteria”. Proceed. Intern. Conf. on Fuzzy Systems, San Francisco, 1993.

    Google Scholar 

  12. A. Cichocki & R. Unbehauen: “Neural Networks for Optimisation and Signal Processing”. Teubner & Wiley, Stuttgart & Chichester, 1993.

    Google Scholar 

  13. Ph. Diamond: “Fuzzy Least Squares”. Inform. Sc. 46 (1988), pp. 141–157.

    Article  MathSciNet  MATH  Google Scholar 

  14. D. Driankov, H. Hellendoorn & M. Reinfrank: “An Introduction to Fuzzy Control”. Springer Verlag, Heidelberg, 1993.

    MATH  Google Scholar 

  15. S. B. Duran & P. L. Odell: “Cluster Analysis: A Survey”. Springer Verlag, Berlin, 1974.

    MATH  Google Scholar 

  16. D. Dubois, H. Prade & R. R. Yager (eds.): “Fuzzy Sets for Intelligent Systems”. Morgan Kaufmann, San Mateo, 1993.

    Google Scholar 

  17. D. E. Heckerman: “Probabilistic Interpretations for MYCIN’S Certainty Factors”. In: L. Kanal & J. Lemmer (eds.): “Uncertainty in Artificial Intelligence”. North Holland Publ., Amsterdam, 1986, pp. 167–196.

    Google Scholar 

  18. S. Horikawa, T. Furuhashi & Y. Ushikawa: “On Fuzzy Modeling using Fuzzy Neural Networks with the Backpropagation Algorithm”. IEEE Transact, on Neural Networks, Vol. 3, No. 5 (1992).

    Google Scholar 

  19. S. K. Halgamuge, A. Mari & M Glesner: “Fast Perceptron Learning by Fuzzy Controlled Dynamic Adaption of Network Parameters”. In: R. Kruse, J. Gebhardt & R. Palm (eds.): “Fuzzy Systems in Computer Science”. Vieweg Verlag, Braunschweig, 1994.

    Google Scholar 

  20. J.-S. Roger Jang: “Self-Learning Fuz2y Controllers based on Temporal Backpropagation”. IEEE Transact. on Neural Networks, Vol. 3, No. 5 (1992), pp. 714–723.

    Article  Google Scholar 

  21. Y. Kodratoff: “Introduction to Machine Learning”. Pitman, 1988.

    MATH  Google Scholar 

  22. B. Kosko: “Neural Networks and Fuzzy Systems”. Prentice Hall, Englewood Cliffs, 1992.

    MATH  Google Scholar 

  23. R. Kruse: “Fuzzy sets — einige Klarstellungen”. KI 4/93 (1993), S. 73–74.

    Google Scholar 

  24. M. Kudra: “Comparison of Methods to Evaluate Fuzzy Parameters in Explicit Functional Relationships”. Freiberger Forschungshefte D 197 (1990), Dt. Verlag f. Grundstoffindustrie, pp. 43–58.

    Google Scholar 

  25. M. Kudra: “A Fuzzy Method to Spectra Interpretation”. In: R. Kruse, J. Gebhardt & R. Palm (eds.): “Fuzzy Systems in Computer Science”. Vieweg Verlag, Braunschweig, 1994.

    Google Scholar 

  26. D. H. Kraft & D. A. Buell: “Fuzzy Sets and Generalized Boolean Retrieval Systems”. In: D. Dubois, H. Prade, R. R. Yager (eds.): “Fuzzy Sets for Intelligent Systems”. Morgan Kaufmann, San Mateo, 1993, pp. 648–659.

    Google Scholar 

  27. L. Köhler & P. Jensch: “Fuzzy Elastic Matching of Medical Objects Using Fuzzy Geometric Representations”. In: R. Kruse, J. Gebhardt & R. Palm (eds.): “Fuzzy Systems in Computer Science”. Vieweg Verlag, Braunschweig, 1994.

    Google Scholar 

  28. F. Klawonn, R. Kruse & D. Nauck: “Neuronale Netze in der KI”. Vieweg Verlag, Braunschweig, 1993.

    Google Scholar 

  29. R. Kruse, K. D. Meyer: “Statistics With Vague Data”. Reidei, Dordrecht, 1987.

    Book  MATH  Google Scholar 

  30. K. V. Mardia, J. T. Kent & J. M. Bibby: “Multivariate Analysis”. Academic Press, London, 1979.

    MATH  Google Scholar 

  31. W. Näther & M. Albrecht: “Linear Regression with Random Fuzzy Observations”. Statistics 21 (1990), pp. 521–531.

    Article  MathSciNet  MATH  Google Scholar 

  32. J. Pearl: “Fusion, Propagation, and Structuring in Belief Networks”. Artific. Intell., Vol. 29 (1986), pp. 241–288.

    Article  MathSciNet  MATH  Google Scholar 

  33. J. Pearl: “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference”. Morgan Kaufmann Publ., San Mateo, 1988.

    Google Scholar 

  34. Yun Peng & J. Reggia: “Abductive Inference Models for Diagnostic Problem-Solving”. Springer Series Symbolic Computation — Artificial Intelligence”. Springer Verlag, Heidelberg, 1990.

    Google Scholar 

  35. J. R. Quinlan: “Discovering Rules from Large Collections of Examples: A Case Study”. In: D. Michie (ed.): “Expert Systems in the Micro-Electronic Age”. Edinburgh University Press, 1979.

    Google Scholar 

  36. E. H. Rusini: “Numerical Methods für Fuzzy Clustering”. In: D. Dubois, H. Prade, R. R. Yager (eds.): “Fuzzy Sets for Intelligent Systems”. Morgan Kaufmann Publ., San Mateo, 1993, pp. 599–614.

    Google Scholar 

  37. J. Schürmann: “PolynomMassifikatoren fur die Zeichenerkennung”. Oldenbourg Verlag, München, 1977.

    Google Scholar 

  38. D. Schoder & H. Geiger: “Das Geheimnis um Neuro-Fuzzy — Kunstliche Intelligenzen profitieren voneinander”. Elektronik plus 2/93 (1993), S. 123–127.

    Google Scholar 

  39. S. R. Sapavian & D. Landgrebe: “A Survey of Decision Tree Classifier Methodology”. IEEE Transact. Systems, Man & Cybern., Vol. 21, No. 3 (1991), pp. 660–674.

    Article  Google Scholar 

  40. P. Shenoy, G. Shafer: “Axioms for Probability and Belief-Function Propagation”. In: R. D. Shachter, T. S. Levttt, L. N. Kanal & J. F. Lemmer (eds.): “Uncertainty in Artificial Intelligence”, Vol. 4. North Holland Publ., Amsterdam, 1990.

    Google Scholar 

  41. K. Sundermeyer: “Knowledge-Based Systems — Terminology and References”. BI Wissenschaftsverlag, Mannheim, 1991.

    MATH  Google Scholar 

  42. R. Viertl: “Einführung in die Statistik”. Kap. VII: “Statistische Analyse für unscharfe Daten”. Springer Verlag, Wien, 1990.

    MATH  Google Scholar 

  43. H.-G. Weil: “Einfühlsames Automobil — Schaltlogik mit integriertem Fuzzy-Regler für ein Automatikgetriebe”. Elektronik plus 2/93 (1993), S. 91–94.

    Google Scholar 

  44. H.-G. Weil, G. Probst & F. Graf: “Fuzzy Shift Logic for an Automatic Transmission System”. Proceed. EUFIT ‘93, Aachen, 1993.

    Google Scholar 

  45. M. Wiemers: “FRED (Fuzzy Preference Decision Support System) — a Knowledge-Based Approach for Fuzzy Multiattribute Preference Decision Making”. In: R. Kruse, J. Gebhardt & R. Palm (eds.): “Fuzzy Systems in Computer Science”. Vieweg Verlag, Braunschweig, 1994.

    Google Scholar 

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Authors
  1. Klaus Dieter Meyer Gramann
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Editors and Affiliations

  1. Institut für Betriebssysteme und Rechnerverbund, Technische Universität Braunschweig, Bültenweg 74/75, 38106, Braunschweig, Germany

    Jörg Gebhardt

  2. Siemens AG, ZFE ST SN 43, Otto-Hahn-Ring 6, 81739, München, Germany

    Rainer Palm

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© 1994 Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden

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Gramann, K.D.M. (1994). Fuzzy Classification: An Overview. In: Kruse, R., Gebhardt, J., Palm, R. (eds) Fuzzy-Systems in Computer Science. Artificial Intelligence / Künstliche Intelligenz. Vieweg+Teubner Verlag. https://doi.org/10.1007/978-3-322-86825-1_22

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  • DOI: https://doi.org/10.1007/978-3-322-86825-1_22

  • Publisher Name: Vieweg+Teubner Verlag

  • Print ISBN: 978-3-322-86826-8

  • Online ISBN: 978-3-322-86825-1

  • eBook Packages: Springer Book Archive

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