Case Base Mining for Adaptation Knowledge Acquisition

TL;DR

This paper introduces CABAMAKA, a data-mining based system for acquiring adaptation knowledge in case-based reasoning, demonstrated through its application to breast cancer treatment decision support.

Contribution

It presents a novel approach to adaptation knowledge acquisition using data-mining techniques within case-based reasoning systems.

Findings

CABAMAKA effectively explores case base variations

Successful application in breast cancer treatment decision support

Enhances adaptation process in case-based reasoning

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.