In this section
@article{Cacciarelli:2024:10.1002/qre.3392,
author = {Cacciarelli, D and Kulahci, M and Tyssedal, JS},
doi = {10.1002/qre.3392},
journal = {Quality and Reliability Engineering International},
pages = {277--296},
title = {Robust online active learning},
url = {http://dx.doi.org/10.1002/qre.3392},
volume = {40},
year = {2024}
}
TY - JOUR
AB - <jats:title>Abstract</jats:title><jats:p>In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in highvolume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional Doptimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.</jats:p>
AU - Cacciarelli,D
AU - Kulahci,M
AU - Tyssedal,JS
DO - 10.1002/qre.3392
EP - 296
PY - 2024///
SN - 0748-8017
SP - 277
TI - Robust online active learning
T2 - Quality and Reliability Engineering International
UR - http://dx.doi.org/10.1002/qre.3392
VL - 40
ER -
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