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Journal articles on the topic 'Concept drift'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Museba, Tinofirei, Fulufhelo Nelwamondo, and Khmaies Ouahada. "ADES: A New Ensemble Diversity-Based Approach for Handling Concept Drift." Mobile Information Systems 2021 (June 1, 2021): 1–17. http://dx.doi.org/10.1155/2021/5549300.

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Beyond applying machine learning predictive models to static tasks, a significant corpus of research exists that applies machine learning predictive models to streaming environments that incur concept drift. With the prevalence of streaming real-world applications that are associated with changes in the underlying data distribution, the need for applications that are capable of adapting to evolving and time-varying dynamic environments can be hardly overstated. Dynamic environments are nonstationary and change with time and the target variables to be predicted by the learning algorithm and oft

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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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Sakurai, Guilherme Yukio, Jessica Fernandes Lopes, Bruno Bogaz Zarpelão, and Sylvio Barbon Junior. "Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives." Future Internet 15, no. 5 (2023): 169. http://dx.doi.org/10.3390/fi15050169.

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The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, which focus on identifying drifts in the data distribution during the operation of a machine learning solution, are a crucial aspect of this paradigm. However, selecting the best change detection method for different types of concept drift can be challenging. This work aimed to provide a benchmark for four drift detection algorithms (EDDM, DDM,

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Toor, Affan Ahmed, Muhammad Usman, Farah Younas, Alvis Cheuk M. Fong, Sajid Ali Khan, and Simon Fong. "Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems." Sensors 20, no. 7 (2020): 2131. http://dx.doi.org/10.3390/s20072131.

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With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are sk

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M, Thangam, Bhuvaneswari A, and Sangeetha J. "A Framework to Detect and Classify Time-based Concept Drift." Indian Journal of Science and Technology 16, no. 48 (2023): 4631–37. https://doi.org/10.17485/IJST/v16i48.583.

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Abstract <strong>Objectives:</strong> To design a framework that performs time series decomposition to detect and classify the types of concept drift in a data stream. The aim of this research is to increase the classification accuracy in the detection and classification of drifts. <strong>Methods:</strong> The proposed method is validated using the Beijing PM2.5 dataset available in the UCI Machine Learning Repository. This dataset has 13 attributes and experiments were performed with the existing drift detection framework algorithms such as EFCDD, Meta-ADD, CIDD, and compariso

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Yang, Lingkai, Sally McClean, Mark Donnelly, Kevin Burke, and Kashaf Khan. "Detecting and Responding to Concept Drift in Business Processes." Algorithms 15, no. 5 (2022): 174. http://dx.doi.org/10.3390/a15050174.

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Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data. Due to factors such as seasonal effects and policy updates, concept drifts can occur in customer transitions and time spent throughout the process, either suddenly or gradually. In a concept drift context, we can discard the old data and retrain the model using new observations (sudden drift) or combine the old data with the new data to update the model (

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Yao, Yuan. "Concept Drift Visualization." Journal of Information and Computational Science 10, no. 10 (2013): 3021–29. http://dx.doi.org/10.12733/jics20101915.

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Webb, Geoffrey I., Roy Hyde, Hong Cao, Hai Long Nguyen, and Francois Petitjean. "Characterizing concept drift." Data Mining and Knowledge Discovery 30, no. 4 (2016): 964–94. http://dx.doi.org/10.1007/s10618-015-0448-4.

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Sun, Yange, Zhihai Wang, Yang Bai, Honghua Dai, and Saeid Nahavandi. "A Classifier Graph Based Recurring Concept Detection and Prediction Approach." Computational Intelligence and Neuroscience 2018 (June 7, 2018): 1–13. http://dx.doi.org/10.1155/2018/4276291.

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It is common in real-world data streams that previously seen concepts will reappear, which suggests a unique kind of concept drift, known as recurring concepts. Unfortunately, most of existing algorithms do not take full account of this case. Motivated by this challenge, a novel paradigm was proposed for capturing and exploiting recurring concepts in data streams. It not only incorporates a distribution-based change detector for handling concept drift but also captures recurring concept by storing recurring concepts in a classifier graph. The possibility of detecting recurring drifts allows re

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Ortíz Díaz, Agustín, José del Campo-Ávila, Gonzalo Ramos-Jiménez, et al. "Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift." Scientific World Journal 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/235810.

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The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification me

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Mauricio Gonçalves Júnior, Paulo, and Sylvain Chartier. "Technique Analysis for Multilayer Perceptrons to Deal with Concept Drift in Data Streams." Interdisciplinary Journal of Information, Knowledge, and Management 19 (2024): 034. https://doi.org/10.28945/5405.

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Aim/Purpose: This paper describes how to use a multilayer perceptron to improve concept drift recovery in streaming environments. Background: Classifying instances in a data stream environment with concept drift is a challenging topic. The base learner must be adapted online to the current data. Several data mining algorithms have been adapted/used to this type of environment. In this study, two techniques are used to speed up the adaptation of an artificial neural network to the current data, increasing its predictive accuracy while detecting the concept drift sooner. Methodology: Experiments

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Sankara Prasanna Kumar, M., A. P. Siva Kumar, and K. Prasanna. "Data Mining Models of High Dimensional Data Streams, and Contemporary Concept Drift Detection Methods: a Comprehensive Review." International Journal of Engineering & Technology 7, no. 3.6 (2018): 148. http://dx.doi.org/10.14419/ijet.v7i3.6.14959.

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Concept drift is defined as the distributed data across multiple data streams that change over the time. Concept drift is visible only when the type of collected data changes after some stable period. The emergence of concept drift in data streams leads to increase misclassification and performing degradation of data streams. In order to obtain accurate results, identification of such concept drifts must be visible. This paper focused on a review of the issues related to identifying the changes occurred in the various multivariate high dimensional data streams. The insight of the manuscript is

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Dries, Anton, and Ulrich Rückert. "Adaptive concept drift detection." Statistical Analysis and Data Mining: The ASA Data Science Journal 2, no. 5-6 (2009): 311–27. http://dx.doi.org/10.1002/sam.10054.

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Muhammad Zaly Shah, Muhammad Zafran, Anazida Zainal, Taiseer Abdalla Elfadil Eisa, Hashim Albasheer, and Fuad A. Ghaleb. "A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer." Mathematics 11, no. 2 (2023): 355. http://dx.doi.org/10.3390/math11020355.

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Data stream mining deals with processing large amounts of data in nonstationary environments, where the relationship between the data and the labels often changes. Such dynamic relationships make it difficult to design a computationally efficient data stream processing algorithm that is also adaptable to the nonstationarity of the environment. To make the algorithm adaptable to the nonstationarity of the environment, concept drift detectors are attached to detect the changes in the environment by monitoring the error rates and adapting to the environment’s current state. Unfortunately, current

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Mahdi, Osama A., Eric Pardede, Nawfal Ali, and Jinli Cao. "Fast Reaction to Sudden Concept Drift in the Absence of Class Labels." Applied Sciences 10, no. 2 (2020): 606. http://dx.doi.org/10.3390/app10020606.

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A data stream can be considered as a sequence of examples that arrive continuously and are potentially unbounded, such as web page visits, sensor readings and call records. One of the serious and challenging problems that appears in a data stream is concept drift. This problem occurs when the relation between the input data and the target variable changes over time. Most existing works make an optimistic assumption that all incoming data are labelled and the class labels are available immediately. However, such an assumption is not always valid. Therefore, a lack of class labels aggravates the

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Museba, Tinofirei, Fulufhelo Nelwamondo, Khmaies Ouahada, and Ayokunle Akinola. "Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts." Applied Computational Intelligence and Soft Computing 2021 (June 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/5533777.

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For most real-world data streams, the concept about which data is obtained may shift from time to time, a phenomenon known as concept drift. For most real-world applications such as nonstationary time-series data, concept drift often occurs in a cyclic fashion, and previously seen concepts will reappear, which supports a unique kind of concept drift known as recurring concepts. A cyclically drifting concept exhibits a tendency to return to previously visited states. Existing machine learning algorithms handle recurring concepts by retraining a learning model if concept is detected, leading to

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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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Wares, Scott, John Isaacs, and Eyad Elyan. "Burst Detection-Based Selective Classifier Resetting." Journal of Information & Knowledge Management 20, no. 02 (2021): 2150027. http://dx.doi.org/10.1142/s0219649221500271.

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Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too frequently when temporal dependence is present can cause classifier performance to appear successful, w

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Lu, Pengqian, Jie Lu, Anjin Liu, and Guangquan Zhang. "Early Concept Drift Detection via Prediction Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19124–32. https://doi.org/10.1609/aaai.v39i18.34105.

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Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in streaming data scenarios. 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. This paper introduces the Prediction Uncertainty Index (PU-index), derived from the prediction uncertainty of the classifier, as a superior alternative to the error rate for drift detection. Our theoretical analysis demonstrates that: (1) T

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Palli, Abdul Sattar, Jafreezal Jaafar, Heitor Murilo Gomes, Manzoor Ahmed Hashmani, and Abdul Rehman Gilal. "An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams." Applied Sciences 12, no. 22 (2022): 11688. http://dx.doi.org/10.3390/app122211688.

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The performance of machine learning models diminishes while predicting the Remaining Useful Life (RUL) of the equipment or fault prediction due to the issue of concept drift. This issue is aggravated when the problem setting comprises multi-class imbalanced data. The existing drift detection methods are designed to detect certain drifts in specific scenarios. For example, the drift detector designed for binary class data may not produce satisfactory results for applications that generate multi-class data. Similarly, the drift detection method designed for the detection of sudden drift may stru

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Hewahi, Nabil M., and Ibrahim M. Elbouhissi. "Concepts Seeds Gathering and Dataset Updating Algorithm for Handling Concept Drift." International Journal of Decision Support System Technology 7, no. 2 (2015): 29–57. http://dx.doi.org/10.4018/ijdsst.2015040103.

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In data mining, the phenomenon of change in data distribution over time is known as concept drift. In this research, the authors introduce a new approach called Concepts Seeds Gathering and Dataset Updating algorithm (CSG-DU) that gives the traditional classification models the ability to adapt and cope with concept drift as time passes. CSG-DU is concerned with discovering new concepts in data stream and aims to increase the classification accuracy using any classification model when changes occur in the underlying concepts. The proposed approach has been tested using synthetic and real datas

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Costa, Albert, Rafael Giusti, and Eulanda M. dos Santos. "Analysis of Descriptors of Concept Drift and Their Impacts." Informatics 12, no. 1 (2025): 13. https://doi.org/10.3390/informatics12010013.

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Concept drift, a phenomenon that can lead to degradation of classifier performance over time, is commonly addressed in the literature through detection and reaction strategies. However, these strategies often rely on complete classifier retraining without considering the properties of the drift, which can prove inadequate in many scenarios. Limited attention has been given to understanding the nature of drift and its characterization, which are crucial for designing effective reaction strategies. Drift descriptors provide a means to explain how new concepts replace existing ones, offering valu

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Barddal, Jean Paul, Heitor Murilo Gomes, and Fabrício Enembreck. "Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory." International Journal of Natural Computing Research 5, no. 1 (2015): 26–41. http://dx.doi.org/10.4018/ijncr.2015010102.

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Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to discard the current concept and adapt to a new one. Ensemble-based drift detection algorithms have been used successfully to the classification task but usually maintain a fixed size ensemble of learners running the risk of needlessly spending processing time and memory. In this paper the authors present improvements to the Scale-free Network Regressor (SFNR), a dynamic ensemble

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Mulimani, Deepa C., Shashikumar G. Totad, and Prakashgoud R. Patil. "Concept Drift Adaptation in Intrusion Detection Systems Using Ensemble Learning." International Journal of Natural Computing Research 10, no. 4 (2021): 1–22. http://dx.doi.org/10.4018/ijncr.2021100101.

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The primary challenge of intrusion detection systems (IDS) is to rapidly identify new attacks, learn from the adversary, and update the intrusion detection immediately. IDS operate in dynamic environments subjected to evolving data streams where data may come from different distributions. This is known as the problem of concept drift. Today's IDS though are equipped with deep learning algorithms most of the times fail to identify concept drift. This paper presents a technique to detect and adapt to concept drifts in streaming data with a large number of features often seen in IDS. The study mo

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YOSHIDA, Kenichi. "Brute force concept drift detection." Procedia Computer Science 225 (2023): 1672–81. http://dx.doi.org/10.1016/j.procs.2023.10.156.

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Babko-Malyi, Sergei. "Ion-drift reactor™ concept." Fuel Processing Technology 65-66 (June 2000): 231–46. http://dx.doi.org/10.1016/s0378-3820(99)00100-9.

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Ju, Chun Hua, and Li Li Mao. "Decision Tree Classification Algorithm within Concept Similarity." Applied Mechanics and Materials 235 (November 2012): 9–14. http://dx.doi.org/10.4028/www.scientific.net/amm.235.9.

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Data stream mining has been applied in many domains, but the concept drifts of data streams bring great obstacles to data mining. Current researches about classification algorithm for streaming data with concept drift have achieved many successes, while they pay little attention to the iterancy of data streams, namely, the situation of the historical concept reappears. For this characteristic, this paper puts forward that it utilizes the classifier model of the historical concepts or high similarity concepts through calculating the concept similarity to classify and predict. In this way, we do

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Gâlmeanu, Honorius, and Răzvan Andonie. "Concept Drift Adaptation with Incremental–Decremental SVM." Applied Sciences 11, no. 20 (2021): 9644. http://dx.doi.org/10.3390/app11209644.

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Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is curr

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Han, Meng, Chunpeng Li, Fanxing Meng, Feifei He, and Ruihua Zhang. "An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift." Applied Sciences 14, no. 16 (2024): 7176. http://dx.doi.org/10.3390/app14167176.

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Learning from multiclass imbalanced data streams with concept drift and variable class imbalance ratios under a limited label budget presents new challenges in the field of data mining. To address these challenges, this paper proposes an adaptive active learning method for multiclass imbalanced data streams with concept drift (AdaAL-MID). Firstly, a dynamic label budget strategy under concept drift scenarios is introduced, which allocates label budgets reasonably at different stages of the data stream to effectively handle concept drift. Secondly, an uncertainty-based label request strategy us

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Hu, Hanqing, and Mehmed Kantardzic. "Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification." Intelligent Decision Technologies 15, no. 4 (2022): 609–22. http://dx.doi.org/10.3233/idt-210115.

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Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algor

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Liu, Shinan, Francesco Bronzino, Paul Schmitt, et al. "LEAF: Navigating Concept Drift in Cellular Networks." Proceedings of the ACM on Networking 1, no. 2 (2023): 1–24. http://dx.doi.org/10.1145/3609422.

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Operational networks commonly rely on machine learning models for many tasks, including detecting anomalies, inferring application performance, and forecasting demand. Yet, model accuracy can degrade due to concept drift, whereby the relationship between the features and the target to be predicted changes. Mitigating concept drift is an essential part of operationalizing machine learning models in general, but is of particular importance in networking's highly dynamic deployment environments. In this paper, we first characterize concept drift in a large cellular network for a major metropolita

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Sandeep Bharadwaj Mannapur. "Understanding Data Drift and Concept Drift in Machine Learning Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 318–30. https://doi.org/10.32628/cseit25111239.

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This comprehensive article examines the critical challenges of data drift and concept drift in machine learning systems deployed across various industries. The article explores how these phenomena affect model performance in production environments, with a particular focus on healthcare, manufacturing, and autonomous systems. The article analyzes different types of drift, including covariate shifts and prior probability shifts, while exploring their manifestations and impacts. Through findings of real-world implementations, the article presents advanced detection methodologies and mitigation s

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Kumar, Sanjeev, Ravendra Singh, Mohammad Zubair Khan, and Abdulfattah Noorwali. "Design of adaptive ensemble classifier for online sentiment analysis and opinion mining." PeerJ Computer Science 7 (August 5, 2021): e660. http://dx.doi.org/10.7717/peerj-cs.660.

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DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detect

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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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Kim, Minsu, Seong-Hyeon Hwang, and Steven Euijong Whang. "Quilt: Robust Data Segment Selection against Concept Drifts." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21249–57. http://dx.doi.org/10.1609/aaai.v38i19.30119.

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Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data streams where the joint distribution of the data X and label y, P(X, y), changes over time and possibly degrade model accuracy. Existing concept drift adaptation approaches mostly focus on updating the model to the new data possibly using ensemble techniques of previous models and tend to discard the drifted historical data. However, we contend that explicitly utilizing the drifted data together leads to much better model

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Priyanka Rajamani and Dr. J. Savitha. "Comparative Analysis of Unsupervised Concept Drift Detection Techniques in High-Dimensional Biomedical Data Streams." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 437–54. https://doi.org/10.32628/cseit25113302.

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In the area of real-time analytics, the ability to detect concept drift shifts in data distribution over time is vital for maintaining the reliability of predictive models. This analysis presents a comprehensive comparative analysis for five Unsupervised Concept Drift Detection Algorithms Adaptive Boosting (AdaBoost), Diversity-Induced Ensemble (DIE), Adaptive Sliding Window (ADWIN), Sequential Probability Ratio Test (SPRT), and Page-Hinkley Test (PHT) with a focus on high-dimensional biomedical data streams. The evaluation is conducted using three large-scale and diverse biomedical datasets:

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Rossetto dos Santos, Eloiza, André Grégio, and Paulo Lisboa de Almeida. "Avaliação de Abordagens para Classificação de Malware Bancário sob a presença de Concept Drift." Anais do Computer on the Beach 16 (May 27, 2025): 053–60. https://doi.org/10.14210/cotb.v16.p053-060.

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AbstractFinancial malware is an increasing threat due to the potential ofprofit for cybercriminals. Year after year, the arms race betweenmalware developers and security professionals foster the release ofmillions of malware variants. Although machine learning techniqueshave been successfully applied to malware classification tasks,the occurrence of concept drifts requires constant updates (or evenretraining) of these models. In this work, we evaluate how distinctmachine learning training approaches for malware classificationwhen we consider the arrival flow of samples as a data stream. Wealso

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Palli, Abdul Sattar, Jafreezal Jaafar, Abdul Rehman Gilal, et al. "Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review." Journal of Information and Communication Technology 23, no. 1 (2024): 105–39. http://dx.doi.org/10.32890/jict2024.23.1.5.

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In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the cla

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Budiman, Arif, Mohamad Ivan Fanany, and Chan Basaruddin. "Adaptive Online Sequential ELM for Concept Drift Tackling." Computational Intelligence and Neuroscience 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/8091267.

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A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change

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Sato, Denise Maria Vecino, Sheila Cristiana De Freitas, Jean Paul Barddal, and Edson Emilio Scalabrin. "A Survey on Concept Drift in Process Mining." ACM Computing Surveys 54, no. 9 (2022): 1–38. http://dx.doi.org/10.1145/3472752.

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Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, data

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Desale, Ketan Sanjay, and Swati Shinde. "Real-Time Concept Drift Detection and Its Application to ECG Data." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 10 (2021): 160. http://dx.doi.org/10.3991/ijoe.v17i10.25473.

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Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to det

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Yang, Rui, Shuliang Xu, and Lin Feng. "An Ensemble Extreme Learning Machine for Data Stream Classification." Algorithms 11, no. 7 (2018): 107. http://dx.doi.org/10.3390/a11070107.

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Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from traditional ELM methods, a concept drift detection method is embedded; it uses online sequence learning strategy to handle gradual concept drift and uses updating classifier to deal with abrupt concept drift, so both gradual concept drift and abrupt concept drift can be detected in this paper. The experimental result

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Gower-Winter, Brandon, Georg Krempl, Sergey Dragomiretskiy, Tineke Jelsma, and Arno Siebes. "Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11726–34. https://doi.org/10.1609/aaai.v39i11.33276.

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Concept Drift has been extensively studied within the context of Stream Learning. However, it is often assumed that the deployed model's predictions play no role in the concept drift the system experiences. Closer inspection reveals that this is not always the case. Automated trading might be prone to self-fulfilling feedback loops. Likewise, malicious entities might adapt to evade detectors in the adversarial setting resulting in a self-negating feedback loop that requires the deployed models to constantly retrain. Such settings where a model may induce concept drift are called performative.

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Althabiti, Mashail, and Manal Abdullah*. "Streaming Data Classification With Concept Drift." Bioscience Biotechnology Research Communications 12, no. 1 (2019): 177–84. http://dx.doi.org/10.21786/bbrc/12.1/20.

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Iwashita, Adriana Sayuri, and Joao Paulo Papa. "An Overview on Concept Drift Learning." IEEE Access 7 (2019): 1532–47. http://dx.doi.org/10.1109/access.2018.2886026.

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Gama, João, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. "A survey on concept drift adaptation." ACM Computing Surveys 46, no. 4 (2014): 1–37. http://dx.doi.org/10.1145/2523813.

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Case, John, Sanjay Jain, Susanne Kaufmann, Arun Sharma, and Frank Stephan. "Predictive learning models for concept drift." Theoretical Computer Science 268, no. 2 (2001): 323–49. http://dx.doi.org/10.1016/s0304-3975(00)00274-7.

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Gonçalves Jr, Paulo Mauricio, and Roberto Souto Maior de Barros. "RCD: A recurring concept drift framework." Pattern Recognition Letters 34, no. 9 (2013): 1018–25. http://dx.doi.org/10.1016/j.patrec.2013.02.005.

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Lifna, C. S., and M. Vijayalakshmi. "Identifying Concept-drift in Twitter Streams." Procedia Computer Science 45 (2015): 86–94. http://dx.doi.org/10.1016/j.procs.2015.03.093.

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Lu, Ning, Guangquan Zhang, and Jie Lu. "Concept drift detection via competence models." Artificial Intelligence 209 (April 2014): 11–28. http://dx.doi.org/10.1016/j.artint.2014.01.001.

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