ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System

TL;DR

This paper introduces ORIS, a reinforcement learning-based method for online active learning that improves streaming analytics by reducing human labeling errors and enhancing model performance.

Contribution

ORIS is a novel Deep Q-Network-based approach for inclusive sampling in online active learning, addressing human errors and boosting streaming analytics accuracy.

Findings

Outperforms traditional baselines in emotion recognition tasks

Reduces human labeling errors significantly

Enhances ML model performance in streaming analytics

Abstract

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.