Skip to Main content Skip to Navigation
New interface
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [7 references]  Display  Hide  Download
Contributor : Fadi Badra Connect in order to contact the contributor
Submitted on : Friday, March 30, 2007 - 3:24:40 PM
Last modification on : Thursday, January 20, 2022 - 4:12:37 PM
Long-term archiving on: : Wednesday, April 7, 2010 - 2:31:51 AM


Publisher files allowed on an open archive




Mathieu d'Aquin, Fadi Badra, Sandrine Lafrogne, Jean Lieber, Amedeo Napoli, et al.. Case Base Mining for Adaptation Knowledge Acquisition. Twentieth International Joint Conference on Artificial Intelligence - IJCAI'07, Jan 2007, Hyderabad, India. pp.750-755. ⟨inria-00127347⟩



Record views


Files downloads