小样本下多稀疏表示分类器的冲突证据融合方法研究
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重庆大学

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TP391

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Research on conflict evidence fusion of multiple sparse representation classifiers in case of small samples
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重庆大学

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    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,提出了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试验中,采用提出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了决策融合,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。

    Abstract:

    The reliability evaluation of sparse representation-based classifier 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.

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历史
  • 收稿日期:2019-12-31
  • 最后修改日期:2021-02-21
  • 录用日期:2020-04-22
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