关键词: 集成自训练算法, 主动学习, 加权K最近邻（KNN）, 朴素贝叶斯, 置信度

Abstract: The self-training algorithm based on ensemble learning is a semi-supervised algorithm. Many scholars choose reliable samples by vote or average confidence of ensemble classifiers. Voting strategies based on confidence tend to choose a sample with high confidence or low confidence but unanimous vote of ensemble classifiers. The latter scenario may mistakenly label a sample near the decision boundary. If the heterogeneous ensemble classifiers are used, it may lead to the problem that a sample of high confidence has different labels labeled by ensemble classifiers. Therefore, unlabeled samples labeled by ensemble classifiers can not be effectively added to the training set. An ensemble self-training algorithm based on active learning and confidence voting is proposed to solve the problems above. The algorithm reasonably adjusts the voting strategy, and labels a unlabelled sample with high confidence and unanimous vote of ensemble classifiers. At the same time, the active learning is used to label samples with low confidence and inconsistent voting of ensemble classifiers, so as to compensate for the shortcoming that the ensemble self-training algorithm focuses only on samples with high confidence, while ignoring useful information of samples of low confidence. The effectiveness of the proposed algorithm is verified by a comparative experiment on the UCI data set.

Key words: ensemble self-training, active learning, weighted K Nearest Neighbor（KNN）, naive Bayes, confidence