Effective labeled data collection plays a critical role in developing and fine-tuning robust streaming analytics systems. However, continuously labeling documents to filter relevant information poses significant challenges like limited labeling budget or lack of high-quality labels. There is a need for efficient human-in-the-loop machine learning (HITL-ML) design to improve streaming analytics systems. One particular HITL- ML approach is online active learning, which involves iteratively selecting a small set of the most informative documents for labeling to enhance the ML model performance. The performance of such algorithms can get affected due to human errors in labeling. To address these challenges, we propose ORIS, a method to perform Online active learning using Reinforcement learning-based Inclusive Sampling of documents for labeling. ORIS aims to create a novel Deep Q-Network-based strategy to sample incoming documents that minimize human errors in labeling and enhance the ML model performance. We evaluate the ORIS method on emotion recognition tasks, and it outperforms traditional baselines in terms of both human labeling performance and the ML model performance.

本研究解决了在流媒体分析系统中有效收集标记数据的挑战，尤其是在预算有限和高质量标签匮乏的情况下。提出的ORIS方法利用基于强化学习的包容性采样策略，有效减少人工标记错误，提升机器学习模型的性能。研究表明，ORIS在情感识别任务中超越了传统基线，为改善流媒体分析系统的设计提供了新的思路。

ORIS：基于强化学习的在线主动学习，采用包容性采样以构建强健的流媒体分析系统