A survey on concept drift adaptation

By João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia. 2014

3,760 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Information Systems

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Citations in the corpus

Recent Advances of Continual Learning in Computer Vision: An Overview (2021, neutral): “Secondly, when learning from new data of the same category in sequential order, this can also lead to the problem of concept drift [3, 4, 5], as the new data may change the data distribution of this category in unforeseen ways [6].”
Incremental Learning in Online Scenario (2020, background): “In addition to address catastrophic forgetting [16] as in ofﬂine incremental learning, we also need to consider concept drift [5] in online scenario.”
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning (2020, background): “This is known task revisiting as recurrent concept drift (Gama et al., 2014).”
Increasing Trust in AI Services through Supplier's Declarations of Conformity (2018, background): “A well-known reason for performance degradation, it is a common cause of frustration and loss of trust for AI service consumers that can be detected and corrected using a multitude of methods [34].”
Continuous Meta-Learning without Tasks (2019, background): “These baselines are a competitive approach to learning in time-varying data streams [13] and have been applied meta-learning in time-varying settings [31].”
Machine learning and deep learning (2021, background): “Further, the validity of recommendations based on data is prone to concept drift, which describes a scenario, where “ the relation between the input data and the target variable changes over time ” (Gama et al. 2014).”
Online Deep Learning: Learning Deep Neural Networks on the Fly (2017, background): “…learn and adapt as and when it sees more data, enabling a form of life-long learning (Lee et al. 2016); (vi) In concept drifting scenarios (Gama et al. 2014), traditional online backpropagation would suffer from slow convergence for deep networks (when the concepts would change), whereas,…”
Learning With Feature Evolvable Streams (2017, background): “We want to emphasize that though concept-drift happens in streaming data where the underlying data distribution changes over time [17], [18], [19], the number of features in concept-drift never changes which is different from our problem.”
FAHT: An Adaptive Fairness-aware Decision Tree Classifier (2019, background): “The main challenge for learning in a stream environment is the so called, concept-drifts, i.e., changes in the joint data distribution over time [Krawczyk et al. , 2017; Aggarwal, 2007; Gama et al. , 2014].”
Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (2019, background): “That is, the joint distribution between the input feature and the ground truth will change as the concept drift [12].”
Incremental on-line learning: A review and comparison of state of the art algorithms (2018, background): “Incremental learning is more frequently treated in the setting of streaming scenarios [17, 18], although most of the work particularly targets concept drift [19, 20, 6].”
Understanding Continual Learning Settings with Data Distribution Drift Analysis (2021, background): “We also build upon the distribution shift literature, notably Kelly et al. (1999); ˇZliobait˙e (2010); Gama et al. (2014).”
Learning with Feature and Distribution Evolvable Streams (2020, background): “Distribution change frequently appears in the streaming data and has drawn considerable research interest in recent years (Gama et al., 2014; Gomes et al., 2017).”
Open challenges for data stream mining research (2014, background): “The challenge posed by drift has been subject to extensive research, thus we provide here solely a brief categorization and refer to recent surveys like [17].”
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees (2022, positive): “The statistical characteristics describing the underlying distribution of instance-label pairs often change with time in practical scenarios of supervised classiﬁcation (Gama et al., 2014; Ditzler et al., 2015).”
Social media big data analytics: A survey (2019, neutral): “However, e ﬃ cient techniques and analytical approaches to handle the ever-growing data generated by various social media applications are of paramount need (Gama, Ž liobait ė , Bifet, Pechenizkiy, & Bouchachia, 2014).”