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A survey of existing methods for stopping active learning (AL) reveals theneeds for methods that are: more widely applicable; more aggressive in savingannotations; and more stable across changing datasets. A new method forstopping AL based on stabilizing predictions is presented that addresses theseneeds. Furthermore, stopping methods are required to handle a broad range ofdifferent annotation/performance tradeoff valuations. Despite this, theexisting body of work is dominated by conservative methods with little (if any)attention paid to providing users with control over the behavior of stoppingmethods. The proposed method is shown to fill a gap in the level ofaggressiveness available for stopping AL and supports providing users withcontrol over stopping behavior.

A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping