Semi-supervised learning in imbalanced sample set classification
半监督学习在不平衡样本集分类中的应用研究

Higher error rate emerged in the minority class of samples when make classification on imbalanced sample set, but most algorithms in semi-supervised learning are based on normal data set. This paper studied the effectiveness of a semi-supervised collaboration classification method. Because of the further enhanced classifier difference, this algorithm had good performance on classification of imbalanced sample set. It established classification model based on the above algorithm, and used this model to make classification with bridge structural health monitoring data, the compared results of which demonstrated the applicability to imbalanced sample set. Therefore it validated the effectiveness of the algorithm.