Traditional active learning tries to identify instances for which the acquisition of the label increases model performance under budget constraints. Less research has been devoted to the task of actively acquiring feature values, whereupon both the instance and the feature must be selected intelligently and even less to a scenario where the instances arrive in a stream with feature drift. We propose an active feature acquisition strategy for data streams with feature drift, as well as an active feature acquisition evaluation framework. We also implement a baseline that chooses features randomly and compare the random approach against eight different methods in a scenario where we can acquire at most one feature at the time per instance and where all features are considered to cost the same. Our initial experiments on 9 different data sets, with 7 different degrees of missing features and 8 different budgets show that our developed methods outperform the random acquisition on 7 data sets and have a comparable performance on the remaining two.

传统的主动学习试图确定在预算约束下获得标签可以提高模型性能的实例。很少有研究致力于主动获取特征值的任务，因此必须智能地选择实例和特征，而对于实例进入具有特征漂移的流的情况则更少。我们针对具有特征漂移的数据流提出了一种主动特征获取策略，以及一种主动特征获取评估框架。我们还实现了一个基线，该基线可以在每个实例一次最多获取一个特征并且所有特征都被认为成本相同的情况下，随机选择特征并将随机方法与八种不同方法进行比较。我们在9种不同的数据集上进行了初步实验，