Multivariate and Univariate Anomaly Detection in Machine Learning: A Bibliometric Analysis
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-31153-6_29
Reference30 articles.
1. Zhou, L., Zeng, Q., Li, B.: Hybrid anomaly detection via multihead dynamic graph attention networks for multivariate time series. IEEE Access 10, 40967–40978 (2022). https://doi.org/10.1109/access.2022.3167640
2. Mehrotra, K.G., Mohan, C.K., Huang, H.: Anomaly Detection Principles and Algorithms. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67526-8
3. Quesada, A.: Outlier Detection. Retrieved from KDNuggets: Methods to deal with Outliers (2017). https://www.kdnuggets.com/2017/01/3-methods-deal-outliers.html
4. Ramchandran, A., Sangaia, A.K.: Unsupervised anomaly detection for high dimensional data-an exploratory analysis. In: Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, pp. 233–251. Elsevier (2018). https://doi.org/10.1016/B978-0-12-813314-9.00011-6
5. Demestichas, K., Alexakis, T., Peppes, N., Adamopoulou, E.: Comparative analysis of machine learning-based approaches for anomaly detection in vehicular data. Vehicles 3(2), 171–186 (2021). https://doi.org/10.3390/vehicles3020011
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