Abstract: To improve the performance of the co-training algorithm and expand the range of applications, a multi-view semi-supervised collaboration classification algorithm with the combination of consistent and inconsistent label rules was proposed, which aimed at providing a more effective method for the classification of the bridge structured health data. The proposed algorithm used combination of agreement and disagreement label rules for the unlabeled data by judging whether the two classifiers were consistent. Put the sample to the label set, if the label results were consistent. If the label results were inconsistent and the confidence was beyond the threshold, it put the label result of the high confidence to the label set, took full use of the unlabeled data to improve the performance of the classifier, and updated the classification model by the difference of the classifiers. The experimental results of the proposed algorithm on the bridge structured health datasets and standard UCI datasets verify the effectiveness and feasibility of the proposed model on the multi-view classification problems.

Key words: multi-view, semi-supervised co-training learning, combined label, classifier difference, bridge structured health monitoring

摘要： 为了提高多视图半监督协同算法的性能，并针对算法应用范围受限的问题，提出了一种组合标记规则的协同训练方法。该算法将一致性与非一致性标记规则相结合，若分类器具有相同标记则将对应样本加入到相应的样本集中；若标记不同且两分类器对应的标记置信度差值超过了一定的阈值，则采用高置信度分类器的标记结果，并将样本添加到相应的样本集中。通过判断两分类器对相应样本的标记是否一致以及差异性阈值对未标记样本进行组合标记，并利用分类器差异性判断原则更新分类模型，充分利用未标记样本中的有用信息将分类器性能提高5%以上。所提出的算法在桥梁结构健康监测数据集及标准UCI数据集上的实验结果验证了算法在多视图分类问题上的有效性和可行性。

关键词: 多视图, 半监督协同学习, 组合标记, 分类器差异, 桥梁结构健康监测