Impact of Batch Size on Stopping Active Learning for Text Classification
TABLE I: Stopping method results on 20Newsgroups for different batch sizes using various window sizes. The top number in each row shows the number of annotations at the stopping point and the bottom number shows the F-Measure at the stopping point.
['[width=15em]Stopping MethodBatch Percent', '1%', '5%', '10%']
We considered different batch sizes in our experiments, based on percentages of the entire set of training data. We ran BV2009 with smaller window sizes for each of our different batch sizes. When using a window size of one, BV2009 is able to stop with a smaller number of annotations than when using a window size of three. This is done without losing much F-Measure. The next subsection provides an explanation as to why smaller window sizes are more effective than larger window sizes when larger batch sizes are used.