Case Base Mining for Adaptation Knowledge Acquisition
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.
Cite
Text
d'Aquin et al. "Case Base Mining for Adaptation Knowledge Acquisition." International Joint Conference on Artificial Intelligence, 2007.Markdown
[d'Aquin et al. "Case Base Mining for Adaptation Knowledge Acquisition." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/daposaquin2007ijcai-case/)BibTeX
@inproceedings{daposaquin2007ijcai-case,
title = {{Case Base Mining for Adaptation Knowledge Acquisition}},
author = {d'Aquin, Mathieu and Badra, Fadi and Lafrogne, Sandrine and Lieber, Jean and Napoli, Amedeo and Szathmary, Laszlo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {750-755},
url = {https://mlanthology.org/ijcai/2007/daposaquin2007ijcai-case/}
}