Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (4): 1071-1077.DOI: 10.11772/j.issn.1001-9081.2020071016

Special Issue: 人工智能

• Artificial intelligence • Previous Articles     Next Articles

Weight allocation and case base maintenance method of case-based reasoning classifier

YAN Aijun1,2,3, WEI Zhiyuan1,2   

  1. 1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
    2. Engineering Research Center of Digital Community, Ministry of Education (Beijing University of Technology), Beijing 100124, China;
    3. Beijing Laboratory for Urban Mass Transit (Beijing University of Technology), Beijing 100124, China
  • Received:2020-07-13 Revised:2020-09-30 Online:2021-04-10 Published:2020-10-19
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61873009), the Beijing Natural Science Foundation (4192009).


严爱军1,2,3, 魏志远1,2   

  1. 1. 北京工业大学 信息学部, 北京 100124;
    2. 数字社区教育部工程研究中心(北京工业大学), 北京 100124;
    3. 城市轨道交通北京实验室(北京工业大学), 北京 100124
  • 通讯作者: 严爱军
  • 作者简介:严爱军(1970—),男,湖北当阳人,教授,博士,主要研究方向:过程建模与控制、人工智能;魏志远(1993—),男,河北唐山人,硕士研究生,主要研究方向:案例推理。
  • 基金资助:

Abstract: As feature weight allocation and case base maintenance have an important influence on the performance of Case-Based Reasoning(CBR) classifier, a CBR algorithm model named Ant lion and Expectation maximization of Gaussian mixture model CBR(AGECBR) was proposed, in which the Ant Lion Optimizer(ALO) was used to allocate weights and Expectation Maximization algorithm of Gaussian Mixture Model(GMMEM) was used for case base maintenance. Firstly, the ALO was used to allocate the feature weights. In this process, the classification accuracy of CBR was used as the fitness function of the ALO to iteratively optimize the feature weights, so as to achive the optimized allocation of feature weights. Secondly, the expectation maximization algorithm of Gaussian mixture model was used to perform clustering analysis to each case in the case base, and the noise cases and redundant cases in the base were deleted, so as to realize the maintenance of the case base. The experiments were carried out on the UCI standard datasets, in which, AGECBR has the average classification accuracy 3.83-5.44 percentage points higher than Back Propagation(BP), k-Nearest Neighbor(kNN) and other classification algorithms. Experimental results show that the proposed method can effectively improve the accuracy of CBR classification.

Key words: Case-Based Reasoning (CBR), weight allocation, case base maintenance, Ant Lion Optimizer (ALO), classifier

摘要: 由于特征权重分配以及案例库维护对案例推理(CBR)分类器的性能有重要影响,提出了用蚁狮(ALO)算法来分配权重且用高斯混合模型的期望最大化算法(GMMEM)进行案例库维护的案例推理算法模型——AGECBR(Ant Lion and Expectation Maximization of Gaussian Mixture Model Case-Based Reasoning)。首先采用蚁狮算法对特征权重进行分配,在这个过程中将案例推理分类准确率作为蚁狮算法对特征权重进行迭代寻优的适应度函数,以此实现特征权重的优化分配;然后,使用高斯混合模型的期望最大化算法对案例库中的各案例进行聚类分析,并删除其中的噪声案例和冗余案例,从而实现案例库的维护。在UCI标准数据集上进行了实验,所提模型AGECBR比反向传播(BP)、k-近邻(kNN)等分类算法平均分类准确率提升了3.83~5.44个百分点。实验结果表明,AGECBR能够使案例推理分类准确率得到有效改进。

关键词: 案例推理, 权重分配, 案例库维护, 蚁狮算法, 分类器

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