关键词: 主动学习, 集成学习, 分歧度, 流数据, HEp-2

Abstract: It is very important to use a large amount of unlabeled samples for efficient learning in date stream environment. The Active Learning based on the disagreement strategy is an effective solution, but usually, the algorithm only considers the largest boundary sample, and neglects the possibility of misjudging of the minimum divergence samples in the earlier stage of training. To achieve the label revision of misjudged samples, a highly efficient learning method integrated with active learning and ensemble learning that based on divergence is proposed. Based on the sample divergence and training stages, different selection strategies for unlabeled sample are adopted by this method. To evaluate the effectiveness of the proposed method, experiments are made on the artificial stream date and HEP-2 cell image. Experimental results show that this method needs less training samples and provides a higher classification precision over the existing Qboost.

Key words: active learning, ensemble learning, divergence level, stream date, HEp-2