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Case-based learning in a bipolar possibilistic framework

✍ Scribed by Jürgen Beringer; Eyke Hüllermeier

Publisher
John Wiley and Sons
Year
2008
Tongue
English
Weight
186 KB
Volume
23
Category
Article
ISSN
0884-8173

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✦ Synopsis

The paper develops a method for case-based learning and prediction within the framework of possibility theory. To this end, a possibilistic version of the similarity-guided extrapolation principle underlying the case-based learning paradigm is proposed. This version goes beyond recent proposals along those lines in that it derives a bipolar characterization of a case-based prediction: The likelihood of each potential output is characterized in terms of both a degree of evidential support and a degree of plausibility. Bipolar possibilistic predictions of such kind are quite appealing from a knowledge representational point of view as they impart much more information than standard case-based predictions. First experimental results showing how the method performs in practice are also presented.

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