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

• URL: http://arxiv.org/abs/2206.12598v1
• Date: Sat, 25 Jun 2022 08:48:25 GMT
• Title: Mitigating sampling bias in risk-based active learning via an EM algorithm
• Authors: Aidan J. Hughes, Lawrence A. Bull, Paul Gardner, Nikolaos Dervilis, Keith Worden
• Abstract summary: Risk-based active learning is an approach to developing statistical classifiers for online decision-support. Data-label querying is guided according to the expected value of perfect information for incipient data points. Semi-supervised approach counteracts sampling bias by incorporating pseudo-labels for unlabelled data via an EM algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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