Classification of Complex-Valued Radar Data using Semi-Supervised Learning: a Case Study
Paper i proceeding, 2023

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.

Complex-Valued Data

Data Labeling

Deep Learning

Automatic Labeling

Complex-Valued Neural Networks

Författare

Teodor Fredriksson

Software Engineering 1

Jan Bosch

Software Engineering 1

Helena Holmström Olsson

Malmö universitet

Proceedings - 2023 49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023

102-107
9798350342352 (ISBN)

49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023
Durres, Albania,

Ämneskategorier

Annan data- och informationsvetenskap

Datavetenskap (datalogi)

DOI

10.1109/SEAA60479.2023.00024

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Senast uppdaterat

2024-02-06