Change Detection in Multivariate Datastreams Controlling False Alarms-Reference-Cited by-同舟云学术

Change Detection in Multivariate Datastreams Controlling False Alarms

Published:2021 Issue: Volume: Page:421-436
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases. Research Track
language:
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Author:

Frittoli Luca,Carrera Diego,Boracchi Giacomo

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-86486-6_26

Reference33 articles.

1. Alippi, C., Boracchi, G., Carrera, D., Roveri, M.: Change detection in multivariate datastreams: likelihood and detectability loss. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), vol. 2, pp. 1368–1374 (2016)

2. Basseville, M., Nikiforov, I.V., et al.: Detection of Abrupt Changes: Theory and Application, vol. 104. Prentice Hall, Englewood Cliffs (1993)

3. Boracchi, G., Carrera, D., Cervellera, C., Macciò, D.: QuantTree: histograms for change detection in multivariate data streams. In: International Conference on Machine Learning, pp. 639–648 (2018)

4. Boracchi, G., Cervellera, C., Macciò, D.: Uniform histograms for change detection in multivariate data. In: 2017 IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1732–1739. IEEE (2017)

5. Carrera, D., Boracchi, G.: Generating high-dimensional datastreams for change detection. Big Data Res. 11, 11–21 (2018)

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