当前位置: X-MOL 学术Theor. Comput. Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Dynamic maintenance case base using knowledge discovery techniques for case based reasoning systems
Theoretical Computer Science ( IF 1.1 ) Pub Date : 2019-07-03 , DOI: 10.1016/j.tcs.2019.06.026
Abir Smiti , Zied Elouedi

The achievement of a Case Based Reasoning (CBR) system is strongly related to the quality of case data and the rapidity of the retrieval process that depends on the quantity of the cases. This quality can diminish especially when the number of cases gets outsized. To guarantee this quality, maintenance the case base becomes essentially. Much existing maintenance CBR approaches focus on the performance of the CBR or the study of the case base (CB) competence. Even though the two points are directly related, there is a few research on using strategies at both points at the same time. Furthermore, the proposed methods are not dynamic, they are not suitable for the frequently change in learning process. In this paper, we propose maintenance CBR method based on well-organized machine learning techniques, in the process of improving the competence and the performance of the CB and can handle incremental cases which evolve over time. We support our approach with empirical evaluation using different benchmark data sets to show the effectiveness of our method.



中文翻译:

使用知识发现技术对基于案例的推理系统进行动态维护的案例库

基于案例的推理(CBR)系统的实现与案例数据的质量以及取决于案例数量的检索过程的快速性密切相关。特别是当案件数量过大时,这种质量可能会降低。为了确保这种质量,维护案例库必不可少。现有的许多维护CBR方法都集中在CBR的性能或案例库(CB)能力的研究上。尽管这两点直接相关,但是有一些关于同时使用两点策略的研究。此外,所提出的方法不是动态的,它们不适合学习过程中的频繁变化。在本文中,我们提出了一种基于组织良好的机器学习技术的维护CBR方法,在提高认证机构的能力和绩效的过程中,可以处理随着时间而发展的增量案件。我们通过使用不同基准数据集的经验评估来支持我们的方法,以证明我们方法的有效性。

更新日期:2019-07-03
down
wechat
bug