HYBRID ACTIVE LEARNING FOR NON-STATIONARY STREAMING DATA WITH ASYNCHRONOUS LABELING

摘要

A continuous electronic data stream of unlabeled data instances is received and fed into both a stream-based selection strategy and a pool-based selection strategy. The stream-based selection strategy is continuously applied to each of the unlabeled data instances to continually select stream-based data instances that are to be annotated. Additionally, the pool-based selection strategy is periodically applied to a pool of data obtained from the unlabeled data instances, to periodically select pool-based data instances that are to be annotated. Each time the pool-based selection strategy is applied, these methods automatically replace the stream-based data instances with the pool-based data instances. Also, these methods provide, on demand, access to allow a user to annotate the stream-based data instances and the pool-based data instances.