Model adaptation in possibilistic instance-based reasoning
This paper extends the possibilistic approach to instance-based reasoning that has recently been developed in a companion paper. Within the framework of this approach, the similarity-guided extrapolation principle underlying instance-based learning is formalized by means of so-called possibility rules, a special type of fuzzy rules. Proceeding from this idea, a methodology has been outlined, which allows a human expert to specify a model of the inference mechanism in a linguistic way. In this paper, a method for adapting a linguistic model automatically to observed data is proposed. This extension frees the expert from specifying mathematical concepts such as similarity measures and membership functions of fuzzy sets precisely. Rather, the expert determines only the qualitative structure of the model, which is then "calibrated" by using the cases stored in memory.
instance-based reasoning fuzzy rules linguistic modeling parameter estimation possibility theory