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Classification of Complex-Valued Radar Data using Semi-Supervised Learning: a Case Study
Chalmers University of Technology, Gothenburg, Sweden.
Chalmers University of Technology, Gothenburg, Sweden.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7700-1816
2023 (English)In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, the interest in applying machine learning (ML) and deep learning (DL) has been increasing due to their ability to learn to predict and find structure in data. The most common approach of ML and DL is supervised learning. Supervised learning requires the input data to be labeled. However, as reported by many industries, such as the embedded systems domain, fully labeled datasets are difficult to obtain since data labeling is manually intensive. This paper uses a semi-supervised learning approach on real-world Pulse-Doppler data obtained from our industry collaborator Saab to address this challenge. We took inspiration from the FixMatch algorithm. To investigate whether unlabeled data can help improve classification accuracy, we compare FixMatch to a supervised baseline. We use five different settings for the number of available labels per class label to investigate how many labeled instances and how much manual effort is required for optimal accuracy. Bayesian Linear Regression is used to analyze the results. The results show that FixMatch can reach a higher accuracy than the supervised baseline. Furthermore, FixMatch requires more computation time but will help reduce manual effort. In addition, FixMatch will not underfit or overfit. Thanks to this study, practitioners know the benefits of utilizing FixMatch and when it is safe to use to improve a supervised baseline in the industry.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023.
Series
Proceedings (EUROMICRO Conference on Software Engineering and Advanced Applications), ISSN 2640-592X, E-ISSN 2376-9521
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-64895DOI: 10.1109/SEAA60479.2023.00024ISBN: 979-8-3503-4235-2 (electronic)ISBN: 979-8-3503-4236-9 (print)OAI: oai:DiVA.org:mau-64895DiVA, id: diva2:1825296
Conference
2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023
Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-01-09Bibliographically approved

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Olsson, Helena Holmström

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