一种改进的案例推理分类方法研究

张春晓; 严爱军; 王普

doi:10.3724/SP.J.1004.2014.02015

一种改进的案例推理分类方法研究

doi: 10.3724/SP.J.1004.2014.02015

1.
北京工业大学电子信息与控制工程学院北京 100124

基金项目:

国家自然科学基金（61374143）

详细信息

作者简介:
张春晓北京工业大学电子信息与控制工程学院博士研究生.主要研究方向为案例推理及其应用.E-mail：zaichunfei123@emails.bjut.edu.cn

通讯作者:
严爱军北京工业大学电子信息与控制工程学院副教授.2006年于东北大学获得博士学位.主要研究方向为人工智能，过程建模与优化控制，本文通信作者.E-mail：yanaijun@bjut.edu.cn

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- 文章访问数: 2100
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出版历程
- 收稿日期: 2013-05-29
- 修回日期: 2014-05-15
- 刊出日期: 2014-09-20

An Improved Classification Approach by Case-based Reasoning

1.
College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124

Funds:

Supported by National Natural Science Foundation of China (61374143)

摘要

摘要: 特征属性的权重分配和案例检索策略对案例推理（Case-based reasoning，CBR）分类的准确率有显著影响. 本文提出一种结合遗传算法、内省学习和群决策思想改进的CBR分类方法. 首先，利用遗传算法得到多组属性权重，再根据内省学习原理对每组权重进行迭代调整；然后，通过案例群检索策略得到满足大多数原则的群决策分类结果；最后，以典型分类数据集的对比实验证明了本文方法能进一步提高CBR分类的准确率. 这表明内省学习可以保证权重分配的合理性，案例群检索策略能充分利用案例库的潜在信息，对提升CBR的学习能力有显著作用.
- 案例推理 /
- 案例检索 /
- 遗传算法 /
- 内省学习 /
- 群决策
Abstract: The distribution of feature attribute weights and the strategy of case retrieval have significant impacts on the classification accuracy of case-based reasoning (CBR). An improved CBR classification approach is proposed, which is combined with genetic algorithms, introspective learning, and group decision-making theory. First, multiple attribute weights are given by a genetic algorithm. Then each group of weights is iteratively adjusted in accordance with the introspective learning principle. After that the group decision-making retrieval result which satisfies the plurality rule can be obtained according to the case group-retrieval strategy. At last, the classification comparison experiments prove that the proposed method could improve the classification accuracy of CBR. The results indicate that the introspective learning could guarantee the rationality of weight allocation, and that the case group-retrieval strategy could make full use of the potential knowledge of case base, having remarkable effects on promoting the learning ability of CBR.
- Case-based reasoning (CBR) /
- case retrieval /
- genetic algorithms /
- introspective learning /
- group decision-making

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参考文献(21)

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一种改进的案例推理分类方法研究

doi: 10.3724/SP.J.1004.2014.02015

作者简介:
张春晓北京工业大学电子信息与控制工程学院博士研究生.主要研究方向为案例推理及其应用.E-mail：zaichunfei123@emails.bjut.edu.cn

通讯作者:
严爱军北京工业大学电子信息与控制工程学院副教授.2006年于东北大学获得博士学位.主要研究方向为人工智能，过程建模与优化控制，本文通信作者.E-mail：yanaijun@bjut.edu.cn

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An Improved Classification Approach by Case-based Reasoning

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一种改进的案例推理分类方法研究

doi: 10.3724/SP.J.1004.2014.02015

作者简介: 张春晓 北京工业大学电子信息与控制工程学院博士研究生.主要研究方向为案例推理及其应用.E-mail：zaichunfei123@emails.bjut.edu.cn

通讯作者: 严爱军 北京工业大学电子信息与控制工程学院副教授.2006年于东北大学获得博士学位.主要研究方向为人工智能，过程建模与优化控制，本文通信作者.E-mail：yanaijun@bjut.edu.cn

计量

出版历程

An Improved Classification Approach by Case-based Reasoning

计量

出版历程

目录

作者简介:
张春晓北京工业大学电子信息与控制工程学院博士研究生.主要研究方向为案例推理及其应用.E-mail：zaichunfei123@emails.bjut.edu.cn

通讯作者:
严爱军北京工业大学电子信息与控制工程学院副教授.2006年于东北大学获得博士学位.主要研究方向为人工智能，过程建模与优化控制，本文通信作者.E-mail：yanaijun@bjut.edu.cn