Case Base Mining for Adaptation Knowledge Acquisition

Mathieu D'Aquin 1 Fadi Badra 2 Sandrine Lafrogne 2 Jean Lieber 2 Amedeo Napoli 2 Laszlo Szathmary 2
2 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMAKA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment.
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Communication dans un congrès
Morgan Kaufmann, Inc. Twentieth International Joint Conference on Artificial Intelligence - IJCAI'07, Jan 2007, Hyderabad, India. pp.750-755, 2007
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Dernière modification le : jeudi 11 janvier 2018 - 06:19:54
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Mathieu D'Aquin, Fadi Badra, Sandrine Lafrogne, Jean Lieber, Amedeo Napoli, et al.. Case Base Mining for Adaptation Knowledge Acquisition. Morgan Kaufmann, Inc. Twentieth International Joint Conference on Artificial Intelligence - IJCAI'07, Jan 2007, Hyderabad, India. pp.750-755, 2007. 〈inria-00127347〉

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