Affiliation:
1. Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark
2. Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway
3. Department of Business Administration Technology and Social Sciences Luleå University of Technology Luleå Sweden
Abstract
AbstractIn 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 high‐volume 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 D‐optimal 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.
Subject
Management Science and Operations Research,Safety, Risk, Reliability and Quality
Reference49 articles.
1. SettlesB.Active learning literature survey. Technical Report 1648 University of Wisconsin‐Madison Department of Computer Science.2009.
2. CacciarelliD KulahciM.A survey on online active learning.2023. arXiv preprint10.48550/arXiv.2302.08893
3. Dynamic soft sensors with active forward-update learning for selection of useful data from historical big database
4. Active learning strategy for smart soft sensor development under a small number of labeled data samples
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