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Case‐based learning in a bipolar possibilistic framework
Author(s) -
Beringer Jürgen,
Hüllermeier Eyke
Publication year - 2008
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20309
Subject(s) - extrapolation , point (geometry) , computer science , degree (music) , artificial intelligence , similarity (geometry) , machine learning , relation (database) , mathematics , data mining , statistics , physics , geometry , acoustics , image (mathematics)
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.