A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
Table 5: Methods for stopping AL with maximum entropy as the base learner. For each stopping method, the average number of annotations at the automatically determined stopping point and the average F-measure at the automatically determined stopping point are displayed. Bold entries are statistically significantly different than SP (and non-bold entries are not). SC2000, the margin exhaustion method, is not shown since it can’t be used with a non-margin-based learner. The final column (labeled ”All”) represents standard fully supervised passive learning with the entire set of training data.
['Task-Dataset', 'SP', 'V2008', 'ZWH2008', 'LS2008', 'All']
The results reinforce our conclusions from the SVM experiments, with SP performing aggressively and all statistically significant differences in performance being in SP ’s favor.