Hybrid Technique and Competence-Preserving Case Deletion Methods for Case Maintenance in Case-Based Reasoning
Hybrid Technique and Competence-Preserving Case Deletion Methods for Case Maintenance in Case-Based Reasoning
复制标题
基于案例推理中案例维护的混合技术和能力保留案例删除方法
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
J. Daengdej
中科院分区：
文献类型：
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作者：
A. Lawanna;J. Daengdej
基于案例的推理（CBR）是一种用于问题求解和学习的机器学习算法，在过去几年中引起了很多关注。一般来说，CBR由四个主要阶段组成：检索最相似的案例或案例，重用案例解决问题，修改或调整建议的解决方案，并保留学习的案例，然后将它们返回到案例库进行学习。不幸的是，在许多情况下，这种保留过程会导致不受控制的案例库增长。该问题在几次运行后影响CBR系统的能力和性能。本文提出了两种案例维护方法，第一种方法是将案例添加策略与足迹删除和足迹效用删除策略相结合的混合技术，第二种方法是能力保持案例删除技术，它包括四个步骤：确定一组目标问题，确定一个候选案例，确定目标问题及其候选，删除不相关的案例。
Case-Based Reasoning (CBR) is one of machine learning algorithms for problem solving and learning that caught a lot of attention over the last few years. In general, CBR is composed of four main phases: retrieve the most similar case or cases, reuse the case to solve the problem, revise or adapt the proposed solution, and retain the learned cases before returning them to the case base for learning purpose. Unfortunately, in many cases, this retain process causes the uncontrolled case base growth. The problem affects competence and performance of CBR systems after few runs. This paper proposes two case maintenance methods; the first method is Hybrid technique which combines case addition strategy and the footprint deletion and footprint utility deletion strategy and the second is competence-preserving case deletion technique which is consisted of four steps: determine a set of target problems, determine a candidate of cases , determine target problem and its candidate, delete less relevant cases.