Using system context information to complement weakly labeled data
This addresses the challenge of labeling inefficiency and deployment sensitivity in large-scale sensor networks, offering a domain-specific solution.
The paper tackled the problem of incomplete and uncertain labels in sensor network datasets by using system context information formalized in an information graph and embedding it via contrastive learning, resulting in increased accuracy for weakly labeled data and improved robustness and transferability to new sensor locations.
Real-world datasets collected with sensor networks often contain incomplete and uncertain labels as well as artefacts arising from the system environment. Complete and reliable labeling is often infeasible for large-scale and long-term sensor network deployments due to the labor and time overhead, limited availability of experts and missing ground truth. In addition, if the machine learning method used for analysis is sensitive to certain features of a deployment, labeling and learning needs to be repeated for every new deployment. To address these challenges, we propose to make use of system context information formalized in an information graph and embed it in the learning process via contrastive learning. Based on real-world data we show that this approach leads to an increased accuracy in case of weakly labeled data and leads to an increased robustness and transferability of the classifier to new sensor locations.