Abstract: Systems and methods of online active learning with corrective and iterative feedback for streaming data classification are provided. In one exemplary embodiment, a method includes initializing trainable parameters of a detection model based on verified samples including a representation of a target event and receiving unverified samples from a client network node device. The method further includes iteratively ranking the unverified samples based on an informativeness criterion, selecting a query subset based on an adaptive query budget, obtaining HITL verification responses for the query subset, and updating the detection model based on the HITL verification responses.

Abstract: Systems and methods of online active learning with corrective and iterative feedback for streaming data classification are provided. In one exemplary embodiment, a method includes initializing trainable parameters of a detection model based on verified samples including a representation of a target event and receiving unverified samples from a client network node device. The method further includes iteratively ranking the unverified samples based on an informativeness criterion, selecting a query subset based on an adaptive query budget, obtaining HITL verification responses for the query subset, and updating the detection model based on the HITL verification responses.