Mitigating sampling bias in risk-based active learning via an EM algorithm

TL;DR

This paper presents a risk-based active learning method using a semi-supervised Gaussian mixture model with an EM algorithm to mitigate sampling bias, improving decision-making in structural health monitoring applications.

Contribution

It introduces a novel semi-supervised approach employing an EM algorithm to reduce sampling bias in risk-based active learning for SHM.

Findings

Effectively reduces sampling bias in active learning

Improves decision-making accuracy in SHM applications

Demonstrates robustness on a numerical example

Abstract

Risk-based active learning is an approach to developing statistical classifiers for online decision-support. In this approach, data-label querying is guided according to the expected value of perfect information for incipient data points. For SHM applications, the value of information is evaluated with respect to a maintenance decision process, and the data-label querying corresponds to the inspection of a structure to determine its health state. Sampling bias is a known issue within active-learning paradigms; this occurs when an active learning process over- or undersamples specific regions of a feature-space, thereby resulting in a training set that is not representative of the underlying distribution. This bias ultimately degrades decision-making performance, and as a consequence, results in unnecessary costs incurred. The current paper outlines a risk-based approach to active learning that utilises a semi-supervised Gaussian mixture model. The semi-supervised approach counteracts sampling bias by incorporating pseudo-labels for unlabelled data via an EM algorithm. The approach is demonstrated on a numerical example representative of the decision processes found in SHM.