Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm

By Dariusz W. Brzezinski, Jerzy Stefanowski. 2014

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “[37], [38], [39], [56], [63] or ensemble methods [7], [9], [27], [44], [46], [51], [54], [61], [70], [72].”
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, methodology): “Another widely used method is ensembling multiple clas-siﬁers [5, 6, 48].”
Ensemble learning for data stream analysis: A survey (2017, methodology): “Another alternative chunk-based approach is the Accuracy Upated Ensemble (AUE) [27,31] .”
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors (2022, background): “Hoeffding Drift Detection Method (HDDM) [97] modifies DDM by using the Hoeffding’s inequality [98] to detect substantial changes in the moving average of the 19 Concept drift detection Windowing technique ADWIN SEED ADWIN2 MDDM ADDM MD3 fsNB EDIST ADDS EDIST2 STEPD FPDD WSTD FSDD CSDD Nacre CALMID S”
Open challenges for data stream mining research (2014, background): “though data streams make evaluation a constant trade-off between several criteria [7].”
Incremental learning algorithms and applications (2016, methodology): “When integrated into robust classifiers such as ensemble techniques, models which can simultaneously deal with different types of drift result [16].”
Learning under Concept Drift: A Review (2019, background): “4 Poker-Hand [108] 1025010 10 10 [13], [32], [64], [74], [104]”
Combination of information entropy and ensemble classification for detecting concept drift in data stream (2018, methodology): “Accuracy Updated Ensemble (AUE2) is one of these approaches that used this mechanism [11].”
Learning in Nonstationary Environments: A Survey (2015, methodology): “Other approaches, such as the accuracy updated ensemble (AUE), follow a similar methodology of examining how to keep/remove classifiers in a fixed ensemble size [91].”
Extremely Fast Decision Tree (2018, background): “The de facto standard for learning decision trees from streaming data is Hoeffding Tree (HT) [11], which is used as a base for many state-of-the-art drift learners [3, 6, 8, 10, 16, 18, 24].”