Incremental Learning of Concept Drift from Streaming Imbalanced Data

By Gregory Ditzler, Robi Polikar. IEEE Transactions on Knowledge and Data Engineering, 2013

385 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b32d65b6208eb581f46b6908225e90513312ad86

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Citations in the corpus

Incremental Evolving Domain Adaptation (2016, methodology): “methods such as [18], [19], [20], [21], [22], [23], [24], [25] are”
IncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection (2020, background): “NSE [31] and its class imbalance variant [32] study incremental learning of concept drift in nonstationary environments.”
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”
Learning in Nonstationary Environments: A Survey (2015, background): “learning algorithm for imbalanced-nonstationary data streams that does not require access to historical data [106], [107].”
Learning under Concept Drift: A Review (2019, methodology): “[119] presented two ensemble methods for learning under concept drift with imbalanced class.”
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, methodology): “…both obstacles from the point of view of preprocessing (Nguyen et al., 2011; He & Chen, 2011; Wang, Minku, & Yao, 2015), particularly using SMOTE (Ditzler & Polikar, 2013), and/or cost-sensitive learning via ensembles of classifiers (Mirza, Lin, & Liu, 2015; Ghazikhani, Monsefi, & Sadoghi Yazdi,…”
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018, methodology): “When data streams are characterized by both concept drift and unbalanced distributions, adaptation is often achieved by combining ensemble methods and resampling techniques [26], [36], [64].”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “CDS [173] rebalances the training sets of each classifier in the ensemble using SMOTE [92].”
Incremental on-line learning: A review and comparison of state of the art algorithms (2018, methodology): “Furthermore, the ORF benefits more from the sub-linear tree complexity because it generates a few deep trees instead of the numerous, shallow ones as done by LPPCART.”
Incremental learning algorithms and applications (2016, methodology): “Ensemble methods have proved particularly useful when dealing with concept drift, with a few popular models ranging from incremental random forests [105], ensembles of bipartite graph classifiers [13], up to advanced weighting schemes suitable for different types of concept drift and recurring conce”