Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing

TL;DR

This paper introduces an ensemble active learning approach using contextual bandits to efficiently select data samples for annotation, enhancing AI model performance in streaming data scenarios.

Contribution

It presents a novel ensemble active learning method leveraging contextual bandits to improve data annotation efficiency in streaming environments.

Findings

Enhanced data annotation efficiency in streaming data

Improved AI model performance through active sample selection

Effective exploration-exploitation balance in data acquisition

Abstract

It is challenging but important to save annotation efforts in streaming data acquisition to maintain data quality for supervised learning base learners. We propose an ensemble active learning method to actively acquire samples for annotation by contextual bandits, which is will enforce the exploration-exploitation balance and leading to improved AI modeling performance.