Title: Stopping Active Learning based on Predicted Change of F Measure for Text Classification,
Abstract: During active learning, an effective stopping method allows users to limit
the number of annotations, which is cost effective. In this paper, a new
stopping method called Predicted Change of F Measure will be introduced that
attempts to provide the users an estimate of how much performance of the model
is changing at each iteration. This stopping method can be applied with any
base learner. This method is useful for reducing the data annotation bottleneck
encountered when building text classification systems.