To construct a classification rule via an active learning method, during the learning process, users select training subjects sequentially, without knowing their labels, based on the model learned at the current stage. For a parametric-model-based classification rule, methods of statistical experimental design are popular guidelines for selecting new learning subjects. However, there is a lack of a counterpart for non-parametric-model-based classifiers, such as support vector machines. Thus, we propose a subject selection scheme via an extended influential index for the area under a receiver operating characteristic curve, which is applicable to general classifiers with continuous scores.

为了通过主动学习方法构造分类规则，在学习过程中，用户基于当前阶段学习的模型，在不知道其标签的情况下顺序选择训练主题。对于基于参数模型的分类规则，统计实验设计方法是选择新学习主题的常用指南。但是，缺少基于非参数模型的分类器（例如支持向量机）的对应项。因此，我们针对接收器工作特性曲线下的区域，通过扩展的影响指数提出了一种主题选择方案，该方案适用于具有连续分数的通用分类器。