Research Article
A Deep Support Vector Machine-Based Outlier Detection Method for Ocean Buoy Data
@INPROCEEDINGS{10.1007/978-3-032-00303-4_5, author={Weiming Xu and Liang Pei}, title={A Deep Support Vector Machine-Based Outlier Detection Method for Ocean Buoy Data}, proceedings={Advanced Hybrid Information Processing. 8th International Conference, ADHIP 2024, Jiaxing, China, September 20--22, 2024, Proceedings, Part III}, proceedings_a={ADHIP}, year={2026}, month={11}, keywords={Deep learning Support vector machines Ocean buoys Data outliers Anomaly detection that}, doi={10.1007/978-3-032-00303-4_5} }- Weiming Xu
Liang Pei
Year: 2026
A Deep Support Vector Machine-Based Outlier Detection Method for Ocean Buoy Data
ADHIP
Springer
DOI: 10.1007/978-3-032-00303-4_5
Abstract
Marine buoy data is an important foundation for marine scientific research, and researchers can use this data to analyze the balance of marine ecosystems, trends in climate change, and so on. However, outliers in ocean buoy data can interfere with the accuracy of research results. In view of this situation, this study proposes an outlier detection method for ocean buoy data based on depth support vector machine technology. First, the buoy data is collected through the sensor installed on the marine buoy and preprocessed; Secondly, data features are extracted to capture useful information related to outlier detection in the data; Finally, the depth support vector machine model is constructed to detect outliers in the buoy data. In testing, it was found that the method performed well in four aspects: accuracy, accuracy, recall and F1 score, which indicates that this method can more effectively and accurately identify the outliers in the ocean buoy data.