小样本下多稀疏表示分类器的决策融合方法  

Decision fusion of multiple sparse representation-based classifiers in case of small samples

作　　者：刘小峰[1] 舒仁杰 柏林[1] 孙兵 LIU Xiao-feng;SHU Ren-jie;BO Lin;SUN Bing(The State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400044

出　　处：《控制与决策》2021年第8期1984-1990,共7页Control and Decision

基　　金：国家自然科学基金项目(51975067,51675064)。

摘　　要：针对稀疏表示分类器的分类性能评估受样本数量影响较大,特别是在小样本情况下其分类精度低导致的强烈证据冲突问题,提出一种基于稀疏表示分类倾向性的决策融合方法.该方法采用稀疏分解重构残差的相对大小对样本在各个类别间的分类倾向性进行量化表征;通过求解残差异同概率,对稀疏分类器的混淆矩阵进行修正,提高了稀疏表示分类器分类性能评估的准确性;利用修正后的混淆矩阵对各个证据源进行加权融合,解决了小样本情况下的辨识精度低导致的高度证据冲突问题.在轴承故障融合诊断实验中,采用提出的方法对不同传感器信号的不同特征向量获得的稀疏表示分类器进行决策融合,达到了轴承故障状态准确辨识的目的,有效验证了所提出方法在小样本情况下进行目标分类识别的有效性与优势性.The reliability evaluation of sparse representation-based classifiers is greatly affected by the number of training samples,resulting in a strong evidence conflict problem in case of small samples.A conflict evidence fusion method for sparse representation-based classifiers is proposed based on the tendency of being classified into different categories.The method uses the sparse reconstruction errors to quantify the classification tendency of samples,and modifies the confusion matrix by solving the probability of difference of reconstruction errors to obtain the probability that the samples are classified into each category.The modified confusion matrix is used to weight and fuse the evidence sources to solve the problem of high evidence conflicts caused by the low accuracies of identification in case of small samples.In the experiment of bearing fault fusion diagnosis,this method is applied to fuse the sparse representation-based classifiers established from different eigenvectors of different observation signals.The experimental results effectively verify the advantages of the proposed method in solving the problem of high-level conflict evidence fusion in case of small samples.

关 键 词：稀疏表示分类器 重构残差 证据冲突 混淆矩阵 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]