Case Base Mining for Adaptation Knowledge Acquisition

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dc.creatorD'Aquin, Mathieu
dc.creatorBadra, Fadi
dc.creatorLafrogne, Sandrine
dc.creatorLieber, Jean
dc.creatorNapoli, Amedeo
dc.creatorSzathmary, Laszlo
dc.date2007-03-30
dc.date.accessioned2026-07-07T07:54:49Z
dc.date.available2026-07-07T07:54:49Z
dc.descriptionIn 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.
dc.identifierhttps://arxiv.org/abs/cs/0703156
dc.identifierhttp://arxiv.org/abs/cs/0703156
dc.identifierDans Twentieth International Joint Conference on Artificial Intelligence - IJCAI'07 (2007) 750--755
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/126756
dc.subjectArtificial Intelligence
dc.titleCase Base Mining for Adaptation Knowledge Acquisition
dc.typetext

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