Stream-based selective sampling is a sophisticated query strategy employed in active learning scenarios dealing with continuous data streams, like those encountered in online or real-time data analysis. This approach enables the algorithm to carefully select a subset from the ongoing data stream. The decision of whether to label this subset is then made in accordance with the current performance state of the model.
If the model is demonstrating high-efficiency and accuracy in processing the current data stream, the algorithm might opt not to label any new data. This choice is aimed at minimizing computational costs. On the contrary, when the model's performance on the current data stream is subpar, the algorithm will likely decide to label new data. The underlying expectation is that this will enhance the model's performance and predictive capabilities.