In the process of iterative training of the classifier by a self-training algorithm,it is difficult to effectively se-lect high-confidence samples and there exists mislabeled samples error accumulation.To address the above issues,this paper proposes a self-training algorithm based on dynamic threshold and difference test.The local outlier factor of the sample is introduced to remove the outliers from the labeled samples,classify and label the unlabeled samples.The un-labeled samples are subsequently fed into the model in batches based on the assigned mark,allowing the model to more easily select high-confidence unlabeled samples.Further,a dynamic membership threshold function is designed based on the changes in the number of newly added pseudo-labeled samples and the contrast membership.This function aims to improve the quality of high-confidence samples.Finally,the dense distance is defined to measure the difference between samples.The sum of dense distances between pseudo-labeled samples and samples of the same class and differ-ent classes is calculated separately to find the pseudo-labeled samples with high uncertainty,and incorporate these samples into the unlabeled samples set of the next round of training,which alleviates error accumulation of mislabeled samples.The experimental results demonstrate effectiveness of this algorithm on 12 benchmark UCI datasets.
维普
万方数据