Abstract
The paper presents a formal framework of instance-based prediction in which the generalization beyond experience is founded on’the concepts of similarity and possibility. The underlying extrapolation principle is formalized by means of possibility rules, a special type of fuzzy rules. Thus, instance-based prediction can be realized as fuzzy set-based approximate reasoning. The basic model is extended by means of fuzzy set-based (linguistic) modeling techniques, including the discounting of untypical cases and the flexible handling and adequate adaptation of different similarity relations. This extension provides a convenient way of incorporating domain-specific (expert) knowledge. Our approach thus allows for combining knowledge and data in a flexible way and favors a view of instance-based reasoning according to which the user interacts closely with the system.
Expanded and updated version of a paper with the same title presented at the 8th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 1575-1582, Madrid, Spain, 2000.
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Hüllermeier, E., Dubois, D., Prade, H. (2002). Knowledge-Based Extrapolation of Cases: A Possibilistic Approach. In: Bouchon-Meunier, B., Gutiérrez-Ríos, J., Magdalena, L., Yager, R.R. (eds) Technologies for Constructing Intelligent Systems 1. Studies in Fuzziness and Soft Computing, vol 89. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1797-3_29
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DOI: https://doi.org/10.1007/978-3-7908-1797-3_29
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