Flexible Control of Case-Based Prediction in the
Framework of Possibility Theory

Didier Dubois, Eyke Hijilermejer, and Henri Prade

Institut de Recherche en Informatique de Toulouse, Universit Paul Sabatier
{dubois , eyke ,prade}@irit.fr




Abstract. The similar problem-similar solution hypothesis underlying 
case-based reasoning is modelled in the framework of possibility theory 
and fuzzy sets. Thus, case-based prediction can be realized in the
form of fuzzy set-based approximate reasoning. The inference process
makes use of fuzzy rules. It is controlled by means of modifier functions
acting on such rules and related similarity measures. Our approach also
allows for the incorporation of domain-specific (expert) knowledge concerning 
the typicality (or exceptionality) of the cases at hand. It thus
favors a view of case-based reasoning according to which the user interacts 
closely with the system in order to control the generalization beyond
observed data. Our method is compared to instance-based learning and
kernel-based density estimation. Loosely speaking, it adopts basic principles 
of these approaches and supplements them with the capability of
combining knowledge and data in a flexible way.
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