@inproceedings{1b0d71cef50f45dc9e662abc00e2687e,
title = "A Semi-supervised Learning Approach for Anomaly Detection in Multidimensional Time Series Data",
abstract = "This paper addresses the problem of anomaly detection in multidimensional time series data, where labeled samples are often scarce and data patterns are complex. We propose a semi-supervised learning algorithm that combines labeled and unlabeled data to enhance detection performance. To mitigate the risk of classifier degradation caused by mislabeled data, we introduce a fuzzy clustering-based selection mechanism. The algorithm selects high-confidence samples from the unlabeled data using fuzzy C-means clustering and iteratively adds them to the training set through a self-training process. Experiments on transformer oil chromatography data show that the proposed method achieves better performance than traditional semi-supervised and supervised approaches. The results demonstrate the effectiveness of integrating clustering techniques into semi-supervised learning for robust anomaly detection under limited label conditions.",
keywords = "Anomaly Detection, Semi-supervised Learning, Time Series Data",
author = "An Wang and Yiming Guan and Mengmeng Li and Hongzhi Wang and Hongqiang Wang and Sijia Zheng and Xiaoqian Meng and Siyan Zhu",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 19th International Conference on Green, Pervasive, and Cloud Computing, GPC 2024 ; Conference date: 27-09-2024 Through 30-09-2024",
year = "2026",
doi = "10.1007/978-981-95-1346-8\_8",
language = "英语",
isbn = "9789819513451",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "123--132",
editor = "Xiaobo Zhou and Chen Yu and Song Guo and Jianping Wang and Xianhua Song and Zeguang Lu",
booktitle = "Green, Pervasive, and Cloud Computing - 19th International Conference, GPC 2024, Proceedings",
address = "德国",
}