Drift Detection

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Most implemented papers archive 2025-07-28

30 shown of 69 papers with code (181 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Automated Machine Learning Techniques for Data Streams
  14 Jun 2021 2 repositories listed
  The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
• Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning
  23 Jan 2019 2 repositories listed Syntology ran 0 of 21 samples · 21 unverified
  Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms…
• Active Anomaly Detection via Ensembles
  17 Sep 2018 2 repositories listed
  First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning.
• McDiarmid Drift Detection Methods for Evolving Data Streams
  5 Oct 2017 2 repositories listed
  Accordingly, concept drifts need to be detected, and handled, as soon as possible.
• Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
  7 Sep 2017 2 repositories listed
  In addition, a number of methods have been developed to detect concept drifts in these streams.
• On the Reliable Detection of Concept Drift from Streaming Unlabeled Data
  31 Mar 2017 2 repositories listed
  On the other hand, unsupervised change detection techniques are unreliable, as they produce a large number of false alarms.
• Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift
  9 Jun 2025 1 repository listed
  The change in data distribution over time, also known as concept drift, poses a significant challenge to the reliability of online learning methods.
• Describing Nonstationary Data Streams in Frequency Domain
  7 Feb 2025 1 repository listed
  The research is followed by the identification of concepts in the real-world data streams.
• The Utility of Hyperplane Angle Metric in Detecting Financial Concept Drift
  12 Jan 2025 1 repository listed
  For instance, our model outperforms the Weighted Increment-Decrement Support Vector Machine (WIDSVM), reducing the error rate by 4% and 1%, respectively.
• Early Concept Drift Detection via Prediction Uncertainty
  15 Dec 2024 1 repository listed
  Although error rate-based concept drift detectors are widely used, they often fail to identify drift in the early stages when the data distribution changes but error rates remain constant.
• Structuring the Processing Frameworks for Data Stream Evaluation and Application
  11 Nov 2024 1 repository listed
  The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications.
• SUDS: A Strategy for Unsupervised Drift Sampling
  5 Nov 2024 1 repository listed
  We also introduce the Harmonized Annotated Data Accuracy Metric (HADAM), a metric that evaluates classifier performance in relation to the quantity of annotated data required to achieve the stated performance, thereby…
• A method to benchmark high-dimensional process drift detection
  5 Sep 2024 1 repository listed
  Process curves are multivariate finite time series data coming from manufacturing processes.
• Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis
  29 Aug 2024 1 repository listed
  This paper introduces a group of novel datasets representing real-time time-series and streaming data of energy prices in New Zealand, sourced from the Electricity Market Information (EMI) website maintained by the New…
• A Synthetic Benchmark to Explore Limitations of Localized Drift Detections
  26 Aug 2024 1 repository listed
  Concept drift is a common phenomenon in data streams where the statistical properties of the target variable change over time.
• DriftGAN: Using historical data for Unsupervised Recurring Drift Detection
  9 Jul 2024 1 repository listed
  In real-world applications, input data distributions are rarely static over a period of time, a phenomenon known as concept drift.
• Online Drift Detection with Maximum Concept Discrepancy
  7 Jul 2024 1 repository listed
  Continuous learning from an immense volume of data streams becomes exceptionally critical in the internet era.
• Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time
  24 Jun 2024 1 repository listed
  Concept Drift is a phenomenon in which the underlying data distribution and statistical properties of a target domain change over time, leading to a degradation of the model's performance.
• Online detection and infographic explanation of spam reviews with data drift adaptation
  21 Jun 2024 1 repository listed
  Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation.
• Concept Drift Visualization of SVM with Shifting Window
  19 Jun 2024 1 repository listed
  This information can be further used to explain the change.
• Concept Drift Detection using Ensemble of Integrally Private Models
  7 Jun 2024 1 repository listed
  In this paper, we focus on the notion of integrally private DNNs to detect concept drifts.
• A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Data
  23 May 2024 1 repository listed
  Uncertain changes in data streams present challenges for machine learning models to dynamically adapt and uphold performance in real-time.
• Unsupervised Concept Drift Detection based on Parallel Activations of Neural Network
  11 Apr 2024 1 repository listed
  Practical applications of artificial intelligence increasingly often have to deal with the streaming properties of real data, which, considering the time factor, are subject to phenomena such as periodicity and more or…
• Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
  12 Feb 2024 1 repository listed
  In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate discriminatory outcomes in decision-making processes.
• Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks
  3 Jan 2024 1 repository listed
  In this work, we explore the potential of model-loss-based and distribution-based drift detection methods to tackle leakage detection.
• A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams
  12 Dec 2023 1 repository listed
  The proposed method leverages a sparse set of initial labels and introduces an innovative drift detection mechanism to enable dynamic model adaptations in response to evolving patterns in the data.
• Efficient Concept Drift Handling for Batch Android Malware Detection Models
  18 Sep 2023 1 repository listed
  Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining.
• Detecting covariate drift in text data using document embeddings and dimensionality reduction
  17 Sep 2023 1 repository listed
  In this research, we investigate the effectiveness of different document embeddings, dimensionality reduction techniques, and drift detection methods for identifying covariate drift in text data.
• Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes
  4 Aug 2023 1 repository listed
  The GAEs minimise the reconstruction error between the original graph and its learned representation during training.
• An efficient and straightforward online quantization method for a data stream through remove-birth updating
  21 Jun 2023 1 repository listed
  The characteristics of a data stream may change dynamically, and this change is known as concept drift.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.