Case-based learning in a bipolar possibilistic framework

Authors:
Jürgen Beringer;Eyke Hüllermeier
Affiliations:
Fakultät für Informatik, Otto-von-Guericke-Universität, Magdeburg, Germany FB Mathematik-Informatik, Philipps-Universität, Marburg, Germany;Fakultät für Informatik, Otto-von-Guericke-Universität, Magdeburg, Germany FB Mathematik-Informatik, Philipps-Universität, Marburg, Germany
Venue:
International Journal of Intelligent Systems - Bipolar Representations of Information and Preference Part 2: Reasoning and Learning
Year:
2008

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Abstract

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. © 2008 Wiley Periodicals, Inc.