Adversarial concept drift detection under poisoning attacks for robust data stream mining

DOI10.1007/s10994-022-06177-wzbMath1525.68117arXiv2009.09497OpenAlexW3087732660WikidataQ114859191 ScholiaQ114859191MaRDI QIDQ6053814

Łukasz Korycki, Bartosz Krawczyk

Publication date: 24 October 2023

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2009.09497

zbMATH Keywords

concept driftadversarial learningdata stream miningBoltzmann machinerobust machine learningpoisoning attacks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Online algorithms; streaming algorithms (68W27)

Cites Work

• Discovery science. 10th international conference, DS 2007, Sendai, Japan, October 1--4, 2007. Proceedings
• \(M\)-estimation, convexity and quantiles
• Estimating mutual information for feature selection in the presence of label noise
• Kappa updated ensemble for drifting data stream mining
• Detecting concept change in dynamic data streams
• An Efficient Learning Procedure for Deep Boltzmann Machines
• Advances in Artificial Intelligence – SBIA 2004

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