Classifier Ensembles for Detecting Concept Change in Streaming Data: Overview and Perspectives

By Ludmila I. Kuncheva. 2008

129 citations recorded by the source corpus.

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Citations in the corpus

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning (2017, neutral): “, using automatic drift detection techniques [11, 40, 84].”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, methodology): “, automatic drift detection techniques [48] may be exploited to detect changes in the data evidence p(X).”
A Survey on Ensemble Learning for Data Stream Classification (2017, background): “Finally, surveys on ensemble classiﬁers do exist for data stream learning [Fern and Gi-van 2003; Kuncheva 2004a; 2008], but recent ﬁndings and developments on the ﬁeld justify an updated review.”
Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm (2014, methodology): “usage, restricted learning and testing time, and one scan of incoming instances [2]–[4].”
A survey on data preprocessing for data stream mining: Current status and future directions (2017, background): “Blips, also known as outliers which should be ignored as the change it represents is random [16].”
Incremental Learning of Concept Drift in Nonstationary Environments (2011, background): “…active scaffolding enhances learning by complexity reduction [ ↔ preprocessing, feature selection], prob-lematizing [7], i.e., ﬁnding conﬂicts between current and prior knowledge [ ↔ drift detecting], and fading [7], [8], preventing redundancy when an environment has been learned [ ↔ pruning].”
On Warm-Starting Neural Network Training (2019, background): “[3] Ludmila I Kuncheva.”
Ensemble learning for data stream analysis: A survey (2017, background): “45, 65, 116], and some have been written several years ago [109, 111, 60].”