Active learning involves selecting a few critical unlabeled samples for manual and credible labeling to improve the performance of the current classifier. The critical step of active learning is the sample selection strategy. Uncertainty sampling is a well-known sample selection strategy, which involves selecting the samples for which the current classifier is uncertain. For the generalized linear model, these samples are usually distributed around the current classification hyperplane. However, uncertain samples include samples near the current classification hyperplane, and samples far from the current classification hyperplane and the labeled samples. Traditional uncertainty sampling fails to describe the latter, and traditional methods are easily affected by outliers. In this paper, belief functions are used to describe the uncertainty that exists in various samples. Furthermore, we propose a sample selection strategy based on belief functions. Experimental results based on benchmark datasets show that the proposed approach outperforms several classical methods. Through this approach, higher classification accuracy can be achieved using the same number of new labeled samples.

主动学习涉及选择一些关键的未标记样本进行手动和可信标记，以提高当前分类器的性能。主动学习的关键步骤是样本选择策略。不确定性采样是一种众所周知的样本选择策略，其中涉及选择当前分类器不确定的样本。对于广义线性模型，这些样本通常分布在当前分类超平面附近。但是，不确定样本包括当前分类超平面附近的样本，以及远离当前分类超平面和标记样本的样本。传统的不确定性采样无法描述后者，传统方法很容易受到异常值的影响。在本文中，置信函数用于描述各种样本中存在的不确定性。此外，我们提出了一种基于信念函数的样本选择策略。基于基准数据集的实验结果表明，该方法优于几种经典方法。通过这种方法，可以使用相同数量的新标记样品实现更高的分类精度。