Stream-based active learning with linear models

TL;DR

This paper introduces a novel stream-based active learning strategy for linear models that efficiently selects data points for labeling, reducing prediction error faster in real-time monitoring scenarios.

Contribution

It proposes a new sequential query strategy inspired by optimal experimental design, tailored for stream-based active learning with linear models.

Findings

Faster reduction in prediction error using the proposed method.

Effective in real-time process monitoring scenarios.

Validated through simulations and Tennessee Eastman Process data.

Abstract

The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.