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Relevant bibliographies by topics / Model Drift Detection

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Academic literature on the topic 'Model Drift Detection'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Model Drift Detection"

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Kumar, Sanjeev, and Ravendra Singh. "Comparative Analysis of Drift Detection Based Adaptive Ensemble Model with Different Drift Detection Techniques." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 49–55. http://dx.doi.org/10.51201/jusst/21/06492.

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Stream data mining is a popular research area these days. The concept drift detection and drift handling are the biggest challenges of stream data mining. Several drift detection algorithms have been developed which can accurately detect various drifts but have the problem of false-positive drift detection. The false-positive drift detection leads to the performance degradation of the classifier because of unnecessary training in between analyses. Classifier ensemble has shown its efficiency for drift detection, drift handling, and classification. But the ensemble classifiers could not detect

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Lin, Chin-Yi, Tzu-Liang (Bill) Tseng, and Tsung-Han Tsai. "A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing." Applied Sciences 15, no. 12 (2025): 6500. https://doi.org/10.3390/app15126500.

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The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. Such legacy approaches cannot fully capture the intricate, multi-modal shifts that either gradually erode product quality or trigger abrupt process disruptions. To surmount these challenges, we present M2D2 (Multi-Machine and Multi-Modal Drift Detection), an end-to-end framework that integrates data preprocessing, baseline modelin

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Zhu, Jiaqi, Shaofeng Cai, Fang Deng, Beng Chin Ooi, and Wenqiao Zhang. "METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection." Proceedings of the VLDB Endowment 17, no. 4 (2023): 794–807. http://dx.doi.org/10.14778/3636218.3636233.

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Real-time analytics and decision-making require online anomaly detection (OAD) to handle drifts in data streams efficiently and effectively. Unfortunately, existing approaches are often constrained by their limited detection capacity and slow adaptation to evolving data streams, inhibiting their efficacy and efficiency in handling concept drift , which is a major challenge in evolving data streams. In this paper, we introduce METER, a novel dynamic concept adaptation framework that introduces a new paradigm for OAD. METER addresses concept drift by first training a base detection model on hist

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Mohan Raja Pulicharla. "Detecting and addressing model drift: Automated monitoring and real-time retraining in ML pipelines." World Journal of Advanced Research and Reviews 3, no. 2 (2019): 147–52. https://doi.org/10.30574/wjarr.2019.3.2.0189.

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As machine learning (ML) models transition from development to deployment, their performance can degrade over time due to changes in underlying data distributions, a phenomenon known as model drift. If left unaddressed, model drift can lead to inaccurate predictions, biased outcomes, and poor business decisions. To mitigate this risk, automated model monitoring and real-time retraining are essential in modern ML pipelines. Model drift can manifest in several forms, including concept drift, where the relationship between features and labels changes; covariate shift, where the distribution of in

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Sobolewski, Piotr. "Concept Drift Detection and Model Selection with Simulated Recurrence and Ensembles of Statistical Detectors." JUCS - Journal of Universal Computer Science 19, no. (4) (2013): 462–83. https://doi.org/10.3217/jucs-019-04-0462.

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The paper presents a concept drift detection method for unsupervised learning which takes into consideration the prior knowledge to select the most appropriate classification model. The prior knowledge carries information about the data distribution patterns that reflect different concepts, which may occur in the data stream. The presented method serves as a temporary solution for a classification system after a virtual concept drift and also provides additional information about the concept data distribution for adapting the classification model. Presented detector uses a developed method cal

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McKay, Helen, Nathan Griffiths, Phillip Taylor, Theo Damoulas, and Zhou Xu. "Bi-directional online transfer learning: a framework." Annals of Telecommunications 75, no. 9-10 (2020): 523–47. http://dx.doi.org/10.1007/s12243-020-00776-1.

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Abstract Transfer learning uses knowledge learnt in source domains to aid predictions in a target domain. When source and target domains are online, they are susceptible to concept drift, which may alter the mapping of knowledge between them. Drifts in online environments can make additional information available in each domain, necessitating continuing knowledge transfer both from source to target and vice versa. To address this, we introduce the Bi-directional Online Transfer Learning (BOTL) framework, which uses knowledge learnt in each online domain to aid predictions in others. We introdu

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Althabiti, Mashail Shaeel, and Manal Abdullah. "CDDM: Concept Drift Detection Model for Data Stream." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 10 (2020): 90. http://dx.doi.org/10.3991/ijim.v14i10.14803.

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<p>Data stream is the huge amount of data generated in various fields, including financial processes, social media activities, Internet of Things applications, and many others. Such data cannot be processed through traditional data mining algorithms due to several constraints, including limited memory, data speed, and dynamic environment. Concept Drift is known as the main constraint of data stream mining, mainly in the classification task. It refers to the change in the data stream underlining distribution over time. Thus, it results in accuracy deterioration of classification models an

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Abhay, Dr. "Automated Drift Detection and Retraining Pipeline for ML Models." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50192.

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Abstract—Machine learning (ML) models deployed in dy- namic, real-world environments are susceptible to performance degradation over time due to concept drift—the phenomenon where the underlying data distribution changes. This poses significant challenges to maintaining model reliability and pre- dictive accuracy in production systems. In this project, we propose a fully automated pipeline for drift detection and model retraining, designed to ensure sustained model performance with minimal human intervention. The pipeline leverages statistical drift monitoring techniques through Evidently AI t

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Balasubramanian, Abhinav. "End-to-end model lifecycle management: An MLOPS framework for drift detection, root cause analysis, and continuous retraining." International Journal of Multidisciplinary Research and Growth Evaluation 1, no. 1 (2020): 92–102. https://doi.org/10.54660/.ijmrge.2020.1.1-92-102.

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Machine learning (ML) models deployed in production environments often experience performance degradation over time due to shifts in data distributions, changes in feature relationships, and evolving problem domains. These issues, commonly referred to as data drift, concept drift, and feature drift, necessitate systematic monitoring and intervention to maintain model accuracy and reliability. This paper presents a structured framework for end-to-end model lifecycle management, incorporating drift detection, root cause analysis (RCA), and continuous retraining. Various techniques for detecting

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Han, Meng, Fanxing Meng, and Chunpeng Li. "Variance Feedback Drift Detection Method for Evolving Data Streams Mining." Applied Sciences 14, no. 16 (2024): 7157. http://dx.doi.org/10.3390/app14167157.

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Learning from changing data streams is one of the important tasks of data mining. The phenomenon of the underlying distribution of data streams changing over time is called concept drift. In classification decision-making, the occurrence of concept drift will greatly affect the classification efficiency of the original classifier, that is, the old decision-making model is not suitable for the new data environment. Therefore, dealing with concept drift from changing data streams is crucial to guarantee classifier performance. Currently, most concept drift detection methods apply the same detect

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Dissertations / Theses on the topic "Model Drift Detection"

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Jin, Chao. "A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969.

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Colpo, Kristie M. "Joint Sensing/Sampling Optimization for Surface Drifting Mine Detection with High-Resolution Drift Model." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17345.

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Approved for public release; distribution is unlimited<br>Every mine countermeasures (MCM) operation is a balance of time versus risk. In attempting to reduce time and risk, it is in the interest of the MCM community to use unmanned, stationary sensors to detect and monitor drifting mines through harbor inlets and straits. A network of stationary sensors positioned along an area of interest could be critical in such a process by removing the MCM warfighter from a threat area and reducing the time required to detect a moving target. Although many studies have been conducted to optimize sensors

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Books on the topic "Model Drift Detection"

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Martin, Peter T. Incident detection algorithm evaluation: Draft final report. Utah Dept. of Transportation, Research Division, 2001.

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Book chapters on the topic "Model Drift Detection"

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Liu, Quanchao, Heyan Huang, and Chong Feng. "Micro-blog Post Topic Drift Detection Based on LDA Model." In Behavior and Social Computing. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-04048-6_10.

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Cal, Piotr, and Michał Woźniak. "Drift Detection and Model Selection Algorithms: Concept and Experimental Evaluation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28931-6_53.

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Melo, Fernanda A., André C. P. L. F. de Carvalho, Ana C. Lorena, and Luís P. F. Garcia. "Model Performance Prediction: A Meta-Learning Approach for Concept Drift Detection." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40725-3_5.

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Abisheg, S., M. R. Gauthama Raman, and Aditya P. Mathur. "Adaptive Data-Driven LSTM Model for Sensor Drift Detection in Water Utilities." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-9743-1_16.

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Hu, Songqiao, Zeyi Liu, and Xiao He. "CADM: Confusion Model-Based Detection Method for Real-Drift in Chunk Data Stream." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34899-0_13.

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Jafseer, K. T., S. Shailesh, and A. Sreekumar. "Modeling Concept Drift Detection as Machine Learning Model Using Overlapping Window and Kolmogorov–Smirnov Test." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5868-7_10.

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Rumayor, Haizea, Itziar Ricondo, Jon Castro del Cid, and Aitor Fernández. "Building on the Principles of LLM Models: Vector-Based Anomaly Detection in Pneumatic Cylinder Systems." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_22.

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Abstract Monitoring critical machine components is a key element for maximizing production while minimizing non-operational costs and unplanned downtimes. Continuous monitoring and anomaly detection techniques are highly relevant for ensuring these operational objectives. Anomaly detection remains an active research area, where researchers are continuously exploring novel algorithms and approaches, from traditional techniques (e.g., regression, decision trees, clustering) to novel deep learning approaches (including foundational models). In general, traditional machine learning processes requi

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Yonekawa, Kei, Shuichiro Haruta, Tatsuya Konishi, Kazuhiro Saito, Hideki Asoh, and Mori Kurokawa. "A Study on Metrics for Concept Drift Detection Based on Predictions and Parameters of Ensemble Model." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94822-1_37.

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Kulkarni, Pallavi, and Roshani Ade. "Logistic Regression Learning Model for Handling Concept Drift with Unbalanced Data in Credit Card Fraud Detection System." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2523-2_66.

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Röck, H., and F. Koschmieder. "Model-Based Phasor Control of a Coriolis Mass Flow Meter (CMFM) for the Detection of Drift in Sensitivity and Zero Point." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00578-7_13.

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Conference papers on the topic "Model Drift Detection"

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Chen, Yijie, and Wei Guo. "Concept drift data stream regression model based on adaptive drift detection and incremental broad learning." In International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2024), edited by Xin Xu and Azlan bin Mohd Zain. SPIE, 2025. https://doi.org/10.1117/12.3061638.

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Liu, Minyao, Pan Wang, Yingchun Ye, and Xuejiao Chen. "Model Uncertainty Based Unsupervised Real-Time Drift Detection in Network Traffic Classification." In 2024 IEEE Cyber Science and Technology Congress (CyberSciTech). IEEE, 2024. https://doi.org/10.1109/cyberscitech64112.2024.00023.

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Jančička, Lukáš, Dominik Soukup, Josef Koumar, Filip Němec, and Tomáš Čejka. "MFWDD: Model-based Feature Weight Drift Detection Showcased on TLS and QUIC Traffic." In 2024 20th International Conference on Network and Service Management (CNSM). IEEE, 2024. https://doi.org/10.23919/cnsm62983.2024.10814630.

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Liu, Wenzheng, Xiang Li, Yongtong Gu, et al. "An Adaptive Hoeffding Tree Model Based on Differential Entropy and Relative Entropy for Concept Drift Detection." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650818.

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Zhang, Runyu, Jian Tang, Tianzheng Wang, and Heng Xia. "CO Emission Prediction Model of MSWI Process Combined Sample Output and Feature Space Semi-Supervised Drift Detection." In 2024 6th International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2024. http://dx.doi.org/10.1109/iai63275.2024.10730619.

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Li, Mingwei, Zimeng Fan, Lei Song, and Lili Guo. "Research on Adaptive Model Pooling Method for Data Stream Anomaly Detection Based on Concept Drift Identification Strategy." In 2025 5th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2025. https://doi.org/10.1109/isctis65944.2025.11066053.

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Wang, Pan, Minyao Liu, Zeyi Li, Zixuan Wang, and Xuejiao Chen. "Unsupervised Real-Time Flow Data Drift Detection Based on Model Logits for Internet of Things Network Traffic Classification." In 2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics. IEEE, 2024. http://dx.doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00054.

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Kim, Bumyoon, and Byeungwoo Jeon. "Exploring Feasibility of Data Drift Detection via In-Stream Data for Vision Models." In 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2025. https://doi.org/10.1109/imcom64595.2025.10857506.

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Cusumano, J. P., D. Chelidze, and A. Chatterjee. "Experimental Application of a Method for Hidden Parameter Tracking in a Slowly Changing, Chaotic System." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-1270.

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Abstract Results are presented of an experimental application of a method for tracking hidden parameters in slowly changing chaotic systems. The method exploits the time scale separation between fast dynamic variables and a slow drifting parameter. Locally linear tracking models are constructed using data from the reference system sampled on a fast time scale, employing delay coordinate embedding. These reference models are used to track parameter drift. The method is successfully applied to a forced oscillator with a two-well potential. The effect of the choice of prediction time interval is

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Panda, Pranoy, Sai Srinivas Kancheti, Vineeth N. Balasubramanian, and Gaurav Sinha. "Interpretable Model Drift Detection." In CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD). ACM, 2024. http://dx.doi.org/10.1145/3632410.3632434.

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Reports on the topic "Model Drift Detection"

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Andrews, Madison, and Austin Mullen. DRiFT Current Mode, Trigger Settings and Flexible Detector Specifications Applied to Scintillator Arrays. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2377691.

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