基于边缘增长的协同训练方法

In order to ensure the disagreements among sub-classifiers and improve the classifier performance, a new semi-supervised ensemble learning method based on margin samples addition, termed M-Co-Forest, is proposed in this paper. When pseudo-labeled samples are selected from unlabeled samples, both the unlabeled samples' labeling confidence level and the position of unlabeled samples in the labeled samples are considered. Only samples located at the margin of the current classifier and the labeling confidence level above the preset threshold can be labeled and join the next round of training. At the same time, the noise learning theory is introduced to guide the training process. When pseudo-labeled samples size is not enough to further improve the classifier performance, the iteration stops. The experimental results on multiple UCI datasets and CTG data show that M-Co-Forest outperforms the comparison algorithms.