Knowledge-based extrapolation of cases: a possibilistic approach

Authors:
Eyke Hüllermeier;Didier Dubois;Henri Prade
Affiliations:
Statistics and Decision Theory, University of Paderborn, Germany;IRIT, Université Paul Sabatier, Toulouse, France;IRIT, Université Paul Sabatier, Toulouse, France
Venue:
Technologies for constructing intelligent systems
Year:
2002

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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.