提高案例推理分类器的可靠性研究

赵辉; 严爱军; 王普

doi:10.3724/SP.J.1004.2014.02029

提高案例推理分类器的可靠性研究

doi: 10.3724/SP.J.1004.2014.02029

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）资助

详细信息

作者简介:
赵辉北京工业大学博士研究生.主要研究方向为人工智能，机器学习及其应用.E-mail：taiyuanjifeng2006@126.com

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.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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出版历程
- 收稿日期: 2013-05-29
- 修回日期: 2013-11-26
- 刊出日期: 2014-09-20

On Improving Reliability of Case-based Reasoning Classifier

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

Funds:

Supported by National Natural Science Foundation of China (61374143)

摘要

摘要: 针对案例推理（Case-based reasoning，CBR）分类器的可靠性问题，本文提出一种改进的案例检索和案例重用方法. 首先在案例检索环节应用注水原理对属性权重进行优化分配，利用每个属性数据的标准差和均值构造拉格朗日函数求得属性权重，并设定重要度阈值指导属性约简；其次在案例重用环节引入基于可信度的重用策略，通过计算目标案例分属于各个类别的可信度大小来确定当前案例的分类结果. 最后通过实验对比，表明本文方法能有效提高分类精度和效率，分类器的可靠性得以保障.
- 案例推理分类器 /
- 可靠性 /
- 可信度 /
- 注水原理
Abstract: To aim at the reliability issue of case-based reasoning (CBR) classifier, improved strategies for case retrieve and case reuse are introduced, respectively. In the retrieve step, a new attribute weight assignment method based on the water-filling principle is proposed to optimize the feature weight; particularly, the Lagrange function is constructed by utilizing the mean value and the standard deviation of each attribute to achieve the weight result, then a weight threshold is set to conduct the attribute reduction. In the reuse step, a confidence-reuse strategy is introduced to improve the efficiency of the classifier by calculating the confidence of the target case that belongs to each class. Simulation experiments show that the proposed methods could increase the classification accuracy and efficiency, which proves that the improved strategies could effectively enhance the reliability of the CBR classifier.
- Case-based reasoning (CBR) classifier /
- reliability /
- confidence-reuse /
- water-filling principle

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参考文献(25)

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