Handling delayed labels in temporally evolving data streams
Joshua Plasse, Niall M. Adams · 2016
Streaming classification is well studied in the machine learning community. In real-world applications labels for previously observed feature vectors may only arrive after appreciable lag - that is, the labels are delayed. These delayed labels are an important aspect of streaming analysis, one that is not properly appreciated or addressed in the literature. This paper provides a taxonomy of delayed labeling and a framework for incorporating such labels into a streaming classifier. We provide a real-world demonstration of the utility of correctly handling delayed labels, in the context of a temporally adaptive linear classifier. This simple illustration shows that appropriately handling delayed labels can lead to an increase in performance, suggesting an opportunity for new research.