On improving reliability of case-based reasoning classifier

被引：0

作者：

Zhao, Hui ^{[1
]}

Yan, Ai-Jun ^{[1
]}

Wang, Pu ^{[1
]}

机构：

[1] College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2014年 / 40卷 / 09期

关键词：

Case-based reasoning (CBR) classifier; Confidence-reuse; Reliability; Water-filling principle;

D O I：

10.3724/SP.J.1004.2014.02029

中图分类号：

学科分类号：

摘要：

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. Copyright © 2014 Acta Automatica Sinica. All rights reserved.

引用

页码：2029 / 2036

页数：7

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