Learning under Concept Drift: A Review

By Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang. 2019

1,857 citations recorded by the source corpus.

Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

Subfields: Artificial Intelligence, Biomedical Engineering

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Citations in the corpus

IIRC: Incremental Implicitly-Refined Classification (2020, background): “Our work is also related to knowledge (or concept) drift, where the statistical properties of the data changes over time and old knowledge can become “irrelevant” [28, 27].”
Mind the Gap: Assessing Temporal Generalization in Neural Language Models (2021, background): “Detecting changes in data streams, also known as concept drift, has a long history in machine learning (Widmer and Kubat, 1996; Kifer et al., 2004; Baena-Garcıa et al., 2006; Dries and Rückert, 2009; Lu et al., 2019).”
Deep Class-Incremental Learning: A Survey (2023, background): “, few-shot [204], imbalanced [136], with noisy label [205], weak supervised [206], multi modal [207], concept drift [13], novel classes [208], [209] etc.”
Towards Personalized Federated Learning (2021, background): “Learning in the presence of concept drift often involve three steps: (i) drift detection (whether drift has occurred); (ii) drift understanding (when, how and where the drift occurs); and (iii) drift adaptation (response to drift) [99].”
On Anytime Learning at Macroscale (2021, background): “iii) Similarly, ALMA relates to concept drift (Lu et al., 2018) because of the sequential nature of the observations.”
Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2021, background): “Ways to characterize or detect changes in a task could be helpful in efficiently updating datasets (Lu et al., 2019; Webb et al., 2018).”
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors (2022, methodology): “Concept drift detection is a component of the concept drift handling framework that activates the concept drift adaptation component, which reacts to the change in the data stream [18].”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “Concept drift: This form of model drift characterizes the phenomenon in which the underlying relationship between X and Y changes in non-stationary environments (Lu et al., 2018).”
Class-Incremental Learning: A Survey (2023, methodology): “We systematically choose 17 methods, including: Replay [128], RMM [144] ( data replay ), GEM [53] ( data regularization ), EWC [73] ( parameter regularization ), AANets [66], FOSTER [16], MEMO [17], DER [15], DyTox [18], L2P [69] ( dynamic networks ), LwF [81], iCaRL [82], PODNET [94], Coil [88] ( k”