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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 historical data to capture recurring central concepts , and then learning to dynamically adapt to new concepts in data streams upon detecting concept drift. Particularly, METER employs a novel dynamic concept adaptation technique that leverages a hypernetwork to dynamically generate the parameter shift of the base detection model, providing a more effective and efficient solution than conventional retraining or fine-tuning approaches. Further, METER incorporates a lightweight drift detection controller, underpinned by evidential deep learning, to support robust and interpretable concept drift detection. We conduct an extensive experimental evaluation, and the results show that METER significantly outperforms existing OAD approaches in various application scenarios.
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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, HDDMW, and HDDMA) for abrupt, gradual, and incremental drift types. To shed light on the capacity and possible trade-offs involved in selecting a concept drift algorithm, we compare their detection capability, detection time, and detection delay. The experiments were carried out using synthetic datasets, where various attributes, such as stream size, the amount of drifts, and drift duration can be controlled and manipulated on our generator of synthetic stream. Our results show that HDDMW provides the best trade-off among all performance indicators, demonstrating superior consistency in detecting abrupt drifts, but has suboptimal time consumption and a limited ability to detect incremental drifts. However, it outperforms other algorithms in detection delay for both abrupt and gradual drifts with an efficient detection performance and detection time performance.
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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 skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments.
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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 detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.
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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 comparisons were performed with the proposed TBD framework. The outcome of this research is aggregated with Classification accuracy. An effective algorithm selection framework is presented that detects and classifies time-based concept drift existing in the data. The temporal aspects of the data are decomposed to determine the algorithm to be applied to detect and classify the types of drifts. Depending on the decomposed levels, three varied algorithms have been applied and used for the effective detection and classification of time-based drifts. <strong>Findings:</strong> The performance of the proposed method is validated using the classification accuracy and compared with the existing drift detection framework algorithms. The proposed framework achieves maximum classification accuracy of 95.24% than all the other existing methods. <strong>Novelty:</strong> A novel framework has been proposed with better classification accuracy for the detection and classification of time-based concept drift. <strong>Keywords</strong>: Feature Selection, Concept Drift, Multiple Drift Detection, Time­series decomposition, Classification Accuracy
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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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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) The PU-index can detect drift even when error rates remain stable. (2) Any change in the error rate will lead to a corresponding change in the PU-index. These properties make the PU-index a more sensitive and robust indicator for drift detection compared to existing methods. We also propose a PU-index-based Drift Detector (PUDD) that employs a novel Adaptive PU-index Bucketing algorithm for detecting drift. Empirical evaluations on both synthetic and real-world datasets demonstrate PUDD’s efficacy in detecting drift in structured and image data.
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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 struggle with detecting incremental drift. Therefore, in this experimental investigation, we seek to investigate the performance of the existing drift detection methods on multi-class imbalanced data streams with different drift types. For this reason, this study simulated the streams with various forms of concept drift and the multi-class imbalance problem to test the existing drift detection methods. The findings of current study will aid in the selection of drift detection methods for use in developing solutions for real-time industrial applications that encounter similar issues. The results revealed that among the compared methods, DDM produced the best average F1 score. The results also indicate that the multi-class imbalance causes the false alarm rate to increase for most of the drift detection methods.
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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 algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.
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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 called simulated recurrence and detector ensembles based on statistical tests. Evaluation is performed on benchmark datasets.
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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 reusing previously learnt models and enhancing the overall learning performance. Extensive experiments on both synthetic and real-world data streams reveal that the approach performs significantly better than the state-of-the-art algorithms, especially when concepts reappear.
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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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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, when in fact this is misleading. In this research, a novel architectural method for determining base classifier resets, Burst Detection-Based Selective Classifier Resetting (BD-SCR), is presented. BD-SCR statistically monitors changes in the temporal dependence of a data stream to determine if a base classifier should be reset for detected drifts. The experimental process compares the predictive performance of state-of-the-art drift detectors in comparison to the “No-Change” detector using BD-SCR to inform and control the resetting decision. Results show that BD-SCR effectively reduces the negative impact of temporal dependence during concept drift detection through a clear negation in the performance of the “No-Change” detector, but is capable of maintaining the predictive performance of state-of-the-art drift detection methods.
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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 currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a model that adjusts the shifting window width using the Hoeffding statistical test. We evaluate AIDSVM performance on both synthetic and real-world drift datasets. Experiments show a significant accuracy improvement when encountering concept drift, compared with similar drift detection models defined in the literature. The AIDSVM is efficient, since it is not retrained from scratch after the shifting window slides.
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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 introduce two variants of BOTL that incorporate model culling to minimise negative transfer in frameworks with high volumes of model transfer. We consider the theoretical loss of BOTL, which indicates that BOTL achieves a loss no worse than the underlying concept drift detection algorithm. We evaluate BOTL using two existing concept drift detection algorithms: RePro and ADWIN. Additionally, we present a concept drift detection algorithm, Adaptive Windowing with Proactive drift detection (AWPro), which reduces the computation and communication demands of BOTL. Empirical results are presented using two data stream generators: the drifting hyperplane emulator and the smart home heating simulator, and real-world data predicting Time To Collision (TTC) from vehicle telemetry. The evaluation shows BOTL and its variants outperform the concept drift detection strategies and the existing state-of-the-art online transfer learning technique.
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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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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 modifies extreme gradient boosting (XGB) algorithm for adaptability of drifts and optimization for large datasets in IDS. The primary objective is to reduce the number of ‘false positives' and ‘false negatives' in the predictions. The method is tested on streaming data of smaller and larger sizes and compared against non-adaptive XGBoost and logistic regression.
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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 the exact position of drift occurrence, so it has to update itself at some fixed interval, which leads to an unnecessary computational burden on the system. Combining the drift detection algorithm with an ensemble classifier can improve the performance and also solve the problems of false-positive drift detection and unnecessary updating of the ensemble classifier. In this paper, a model is proposed that creates a weighted adaptive ensemble classifier by updating it only when a drift detection signal is given by the used drift detection method. The proposed model is evaluated on text-based stream data for sentiment analysis and opinion mining with multiple drift detection algorithms and with multiple classification algorithms as base classifiers for the ensemble. A comparative analysis has been done, and the results have shown the efficiency of the proposed models.
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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 detection strategy to different data streams, with little attention to the uniqueness of each data stream. This limits the adaptability of drift detectors to different environments. In our research, we designed a unique solution to address this issue. First, we proposed a variance estimation strategy and a variance feedback strategy to characterize the data stream’s characteristics through variance. Based on this variance, we developed personalized drift detection schemes for different data streams, thereby enhancing the adaptability of drift detection in various environments. We conducted experiments on data streams with various types of drifts. The experimental results show that our algorithm achieves the best average ranking for accuracy on the synthetic dataset, with an overall ranking 1.12 to 1.5 higher than the next-best algorithm. In comparison with algorithms using the same tests, our method improves the ranking by 3 to 3.5 for the Hoeffding test and by 1.12 to 2.25 for the McDiarmid test. In addition, they achieve a good balance between detection delay and false positive rates. Finally, our algorithm ranks higher than existing drift detection methods across the four key metrics of accuracy, CPU time, false positives, and detection delay, meeting our expectations.
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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 probing the inbuilt difficulties of existing contemporary change-detection methods when they encounter during data dimensions scales.
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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-based method for regression that employs social networks theory. In order to detect concept drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show improvements in accuracy, especially in concept drift situations and better performance compared to other state-of-the-art algorithms in both real and synthetic data.
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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 and wrong predictions. Spam emails, consumer behavior changes, and adversary activates, are examples of Concept Drift. In this paper, a Concept Drift detection model is introduced, Concept Drift Detection Model (CDDM). It monitors the accuracy of the classification model over a sliding window, assuming the decline in accuracy indicates a drift occurrence. A modification over CDDM is a weighted version of the CDDM as W-CDDM.</p><p>Both models have evaluated against two real datasets and four artificial datasets. The experimental results of abrupt drift show that CDDM, W-CDDM outperforms the other models in the dataset of 100K and 1M instances, respectively. Regarding gradual drift, the W-CDDM overtook the rest in terms of accuracy, run time, and detection delays in the dataset of 100 K instances. While in the dataset of 1M instances, CDDM has got the highest accuracy using the NB classifier. Moreover, W-CDDM achieves the highest accuracy on real datasets.</p>
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Chu, Renjie, Peiyuan Jin, Hanli Qiao, and Quanxi Feng. "Intrusion detection in the IoT data streams using concept drift localization." AIMS Mathematics 9, no. 1 (2023): 1535–61. http://dx.doi.org/10.3934/math.2024076.
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<abstract><p>With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.</p></abstract>
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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. In this work, we investigate this phenomenon. Our contributions are as follows: First, we define performative drift within a stream learning setting and distinguish it from other causes of drift. We introduce a novel type of drift detection task, aimed at identifying potential performative concept drift in data streams. We propose a first such performative drift detection approach, called CheckerBoard Performative Drift Detection (CB-PDD). We apply CB-PDD to both synthetic and semi-synthetic datasets that exhibit varying degrees of self-fulfilling feedback loops. Results are positive with CB-PDD showing high efficacy, low false detection rates, resilience to intrinsic drift, comparability to other drift detection techniques, and an ability to effectively detect performative drift in semi-synthetic datasets. Secondly, we highlight the role intrinsic (traditional) drift plays in obfuscating performative drift and discuss the implications of these findings as well as the limitations of CB-PDD.
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S, Subha, and G. R. Sathiaseelan J. "Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique in IoT Healthcare Data." Indian Journal of Science and Technology 17, no. 5 (2024): 386–96. https://doi.org/10.17485/IJST/v17i5.1645.
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Abstract <strong>Objectives:</strong> This study addresses the concept drift issue in anomaly detection for IoT systems. The objective is to develop a novel approach that effectively handles the dynamic nature of IoT data.<strong> Methods:</strong> The proposed COMCADSET (Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique) addresses the concept drift challenge. It adapts to evolving data distributions, detects anomalies in IoT healthcare data, mitigates class distribution imbalances through under-sampling, and enhances performance with ensemble techniques. The approach involves four phases: multi-class anomaly spotting, one-class anomaly isolation, concept-drift-free dataset creation, and robust anomaly detection using ensembles. Evaluation utilizes the "Heart Failure Prediction" dataset from Kaggle, with comprehensive experiments and three classification algorithms. COMCADSET's innovation merges one-class and multi-class anomaly detection, under-sampling, and ensemble classification. It's compared against gold standards for classification accuracy, concept drift management, and anomaly detection performance. <strong>Findings:</strong> Conduct comprehensive experiments using a concept drift dataset and three classification algorithms to evaluate the efficacy of the COMCADSET technique. The experimental result shows the proposed COMCADSET technique attains an impressive 98.401% accuracy, decisively enhancing classification accuracy by adeptly addressing concept drift and identifying anomalies in IoT data. Early detection of abnormal behaviour prevents more significant issues and potential security vulnerabilities in IoT systems.<strong> Novelty:</strong> The novelty of the COMCADSET technique lies in its ability to address the concept drift issue and improve anomaly detection accuracy in IoT systems. By integrating one-class and multi-class anomaly detection, under-sampling, and ensemble techniques, the proposed approach provides a robust solution for handling the dynamic nature of IoT data. <strong>Keywords:</strong> Anomaly Detection, Concept Drift, Ensemble Classification, Internet of Things, Under­Sampling
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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 valuable insights into the nature of drift. In this context, this work examines the impact of four descriptors—severity, recurrence, frequency, and speed—on concept drift through extensive theoretical and experimental analysis. Experiments were conducted on five datasets with 32 descriptor variations, eight drift detectors, and a non-detection context, resulting in 1440 combinations. The findings reveal three key conclusions: (i) reaction strategies must be tailored to different types of drift; (ii) severity, recurrence, and frequency descriptors have the highest impact, whereas speed has minimal influence; and (iii) there is a need to incorporate mechanisms for describing concept drift into the strategies designed to address it.
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Sheluhin, Oleg I., Vyacheslav V. Barkov, and Airapet G. Simonyan. "Concept drift detection in mobile applications classification using autoencoders." H&ES Research 15, no. 3 (2023): 20–29. http://dx.doi.org/10.36724/2409-5419-2023-15-3-20-29.
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The study observes the task of concept drift detection in multiclass applications classification tasks on the example of collected data set of network traffic in the form of IP packets from Purpose of the study: development and software implementation of an algorithm for a concept change detection in tasks of multiclass mobile application traffic classification using ANNs of the autoencoder type (AC). Novelty of the study consists in drift detection of one or several mobile applications based on changes in the statistical characteristics of one or several attributes without usage of true class labels implying ANNs of the autoencoder type. Results: The study developed an algorithm for concepts drift of application detection based on the analysis of changes in the statistical characteristics of attributes or a noticeable decrease in the quality of the analyzed applications classification. As for fundamental model of concept drift detector of analyzed applications, the study used autoencoders. The research contains basic theoretical positions of the algorithm creation. The study shows that in case of trained AC only on high-quality prototypes, it will be able to reconstruct normal observations but not abnormal observations (unknown concepts). As a result, when the autoencoder detects a significant reconstruction error, it classifies the observation data as abnormal. Estimation of reconstruction errors of the analyzed applications and excess of threshold value assess the presence of drift. The Python software environment provides the implementation of the presented solution.
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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 strategies, ranging from statistical approaches to sophisticated monitoring frameworks. The investigation extends to emerging technologies in sustainable manufacturing and edge computing environments, offering insights into future developments in drift management. The findings emphasize the importance of proactive drift detection and adaptive model maintenance for ensuring continued system reliability and performance.
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Subha, S., and J. G. R. Sathiaseelan. "Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique in IoT Healthcare Data." Indian Journal Of Science And Technology 17, no. 5 (2024): 386–96. http://dx.doi.org/10.17485/ijst/v17i5.1645.
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Objectives: This study addresses the concept drift issue in anomaly detection for IoT systems. The objective is to develop a novel approach that effectively handles the dynamic nature of IoT data. Methods: The proposed COMCADSET (Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique) addresses the concept drift challenge. It adapts to evolving data distributions, detects anomalies in IoT healthcare data, mitigates class distribution imbalances through under-sampling, and enhances performance with ensemble techniques. The approach involves four phases: multi-class anomaly spotting, one-class anomaly isolation, concept-drift-free dataset creation, and robust anomaly detection using ensembles. Evaluation utilizes the "Heart Failure Prediction" dataset from Kaggle, with comprehensive experiments and three classification algorithms. COMCADSET's innovation merges one-class and multi-class anomaly detection, under-sampling, and ensemble classification. It's compared against gold standards for classification accuracy, concept drift management, and anomaly detection performance. Findings: Conduct comprehensive experiments using a concept drift dataset and three classification algorithms to evaluate the efficacy of the COMCADSET technique. The experimental result shows the proposed COMCADSET technique attains an impressive 98.401% accuracy, decisively enhancing classification accuracy by adeptly addressing concept drift and identifying anomalies in IoT data. Early detection of abnormal behaviour prevents more significant issues and potential security vulnerabilities in IoT systems. Novelty: The novelty of the COMCADSET technique lies in its ability to address the concept drift issue and improve anomaly detection accuracy in IoT systems. By integrating one-class and multi-class anomaly detection, under-sampling, and ensemble techniques, the proposed approach provides a robust solution for handling the dynamic nature of IoT data. Keywords: Anomaly Detection, Concept Drift, Ensemble Classification, Internet of Things, UnderSampling
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LEE, Jeonghoon, and Yoon-Joon LEE. "Concept Drift Detection for Evolving Stream Data." IEICE Transactions on Information and Systems E94-D, no. 11 (2011): 2288–92. http://dx.doi.org/10.1587/transinf.e94.d.2288.
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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 detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world &amp; time-series datasets.
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Mehmood, Tajwar, Seemab Latif, Nor Shahida Mohd Jamail, Asad Malik, and Rabia Latif. "LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing." PeerJ Computer Science 10 (January 31, 2024): e1827. http://dx.doi.org/10.7717/peerj-cs.1827.
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This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and real-world cloud datasets, stressing the need for appropriate drift detectors tailored to the cloud domain. A modified version of Long Short-Term Memory (LSTM) called the LSTM Drift Detector (LSTMDD) is proposed and compared with other top drift detection techniques using prediction error as the primary evaluation metric. LSTMDD is optimized to improve performance in detecting anomalies in non-Gaussian distributed cloud environments. The experiments show that LSTMDD outperforms other methods for gradual and sudden drift in the cloud domain. The findings suggest that machine learning techniques such as LSTMDD could be a promising approach to addressing the problem of concept drift in cloud computing, leading to more efficient resource allocation and improved performance.
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Beshah, Yonas Kibret, Surafel Lemma Abebe, and Henock Mulugeta Melaku. "Drift Adaptive Online DDoS Attack Detection Framework for IoT System." Electronics 13, no. 6 (2024): 1004. http://dx.doi.org/10.3390/electronics13061004.
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Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic environment. The dynamicity of heterogeneous IoT data causes concept drift issues that result in performance degradation and automation difficulties in detecting DDoS. In this study, we propose an adaptive online DDoS attack detection framework that detects and adapts to concept drifts in streaming data using a number of features often used in DDoS attack detection. This paper also proposes a novel accuracy update weighted probability averaging ensemble (AUWPAE) approach to detect concept drift and optimize zero-day DDoS detection. We evaluated the proposed framework using IoTID20 and CICIoT2023 dataset containing benign and DDoS traffic data. The results show that the proposed adaptive online DDoS attack detection framework is able to detect DDoS attacks with an accuracy of 99.54% and 99.33% for the respective datasets.
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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 were performed to analyze how some techniques behave in different scenarios and compare them with other classifiers built to deal with data streams and concept drifts. Contribution: This study suggests two techniques to improve the classification results: an embedded concept drift detection method to identify when a change has occurred and setting the learning rate to a higher level whenever a new concept is being learned to give more weight to recent instances, with its value decreased over time. Findings: Results indicate that gradually reducing the learning rate with an embedded concept drift detector has better statistical results than other single classifiers built to deal with data streams and concept drifts. Recommendations for Practitioners: Based on the empirical results, this study provides recommendations on how to improve the multilayer perceptron in data stream environments suffering from concept drifts. Recommendation for Researchers: Researchers should conduct investigations to increase the number of base classifiers used in data stream environments and in situations where concept drifts occur. Impact on Society: The objective of this study is to increase the use of multilayer perceptrons in data stream environments suffering from concept drifts, as nowadays, Hoeffding Trees and Naive Bayes are the base classifiers mostly used. Future Research: Additional research includes adapting the online learning rate by increasing/decreasing it based on the performance of the Multilayer Perceptron. This scheme would allow the removal of parameters that must be set by the user, like learning rate upper bound and number of instances to return to the stable value.
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Vasilieva, Ivan, and Olga Petrov. "An Empirical Survey of Fully Unsupervised Drift Detection Algorithms for Data Streams." International journal of data science and machine learning 05, no. 01 (2025): 20–28. https://doi.org/10.55640/ijdsml-05-01-05.
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This paper presents a comprehensive benchmark and survey of fully unsupervised concept drift detectors (UCDD) designed to identify and adapt to concept drift in real-world data streams. Concept drift refers to the phenomenon where the statistical properties of a data stream change over time, leading to the deterioration of model accuracy if not detected and adjusted. The study reviews the state of the art in UCDDs, evaluates their performance on various real-world datasets, and identifies challenges and open research areas in the field. Through empirical experiments and a systematic review of existing methods, we highlight key factors influencing the performance of these detectors in unsupervised environments.
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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: MIMIC-III/IV, UK Biobank, and MedMNIST, each representing a distinct challenge in terms of dimensionality, temporal variability and data type (tabular, genomic, and imaging). Performance is assessed across key metrics including Detection Delay, Memory Usage, Execution Time, and post-drift classification effectiveness (Precision, Recall, F1-Score, and Accuracy). Both synthetic and real-world drifts are incorporated to simulate dynamic environments. The findings reveal that ensemble-based methods such as AdaBoost and DIE outperform statistical approaches in handling noisy, sparse, and high-dimensional streams, offering superior adaptability and robustness. This research contributes a systematic evaluation framework and empirical insights to guide the deployment of unsupervised drift-aware systems in healthcare and other data-intensive domains.
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Hu, Lisha, Yaru Lu, and Yuehua Feng. "Concept Drift Detection Based on Deep Neural Networks and Autoencoders." Applied Sciences 15, no. 6 (2025): 3056. https://doi.org/10.3390/app15063056.
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In domains such as fraud detection, healthcare, and industrial equipment maintenance, streaming data often exhibit characteristics such as continuous generation, high real-time processing requirements, and complex distributions, making it susceptible to concept drift. Traditional shallow models, with their limited representational capacity, struggle to fully capture the latent conceptual knowledge inherent in the dynamic and evolving nature of streaming data. To address this challenge, we propose a concept drift detection method based on deep neural networks combined with autoencoders (Concept Drift Detection Based on Deep Neural Network Combined with Autoencoder, DNN+AE-DD). In the DNN+AE-DD, a deep neural network is first employed as the base model for pretraining, and the hidden layer parameters of the model are transferred to a network with an identical structure for stream data processing, where certain hidden layers are frozen. Subsequently, the hidden layer outputs from both the pretraining and stream data processing phases are collected and used as training and testing data to initialize and predict using an autoencoder model. Concept drift is then detected by combining the reconstruction error of the autoencoder with the 3σ principle. Experimental results on both real-world and synthetic datasets demonstrate that, compared to traditional shallow concept drift detection methods, this approach effectively and accurately detects anomalies in streaming data, confirming the proposed model’s high sensitivity to concept drift.
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Adebayo, Oluwadare Samuel, Thompson Aderonke Favour-Bethy, Owolafe Otasowie, and Orogun Adebola Okunola. "Comparative Review of Credit Card Fraud Detection using Machine Learning and Concept Drift Techniques." International Journal of Computer Science and Mobile Computing 12, no. 7 (2023): 24–48. http://dx.doi.org/10.47760/ijcsmc.2023.v12i07.004.
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Credit card fraud is a significant concern for financial institutions and cardholders alike. As fraudulent activities become more sophisticated, traditional rule-based approaches struggle to keep up. This has led to the adoption of machine learning techniques for fraud detection, which have shown promising results. However, the dynamic nature of credit card fraud poses a challenge due to the concept drift phenomenon. Concept drift refers to the changes in the underlying data distribution over time, requiring models to adapt and evolve to maintain their effectiveness. This research paper aims to provide a comprehensive comparative review of credit card fraud detection methods using machine learning and concept drift techniques. This literature review provides an overview of relevant studies comparing credit card fraud detection using machine learning techniques and concept drift handling methods. The paper evaluates the performance, strengths, and limitations of various approaches in addressing credit card fraud detection under concept drift scenarios.
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Li, Xiangjun, Yong Zhou, Ziyan Jin, Peng Yu, and Shun Zhou. "A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance." Journal of Electrical and Computer Engineering 2020 (March 19, 2020): 1–8. http://dx.doi.org/10.1155/2020/4027423.
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Data stream mining has become a research hotspot in data mining and has attracted the attention of many scholars. However, the traditional data stream mining technology still has some problems to be solved in dealing with concept drift and concept evolution. In order to alleviate the influence of concept drift and concept evolution on novel class detection and classification, this paper proposes a classification and novel class detection algorithm based on the cohesiveness and separation index of Mahalanobis distance. Experimental results show that the algorithm can effectively mitigate the impact of concept drift on classification and novel class detection.
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Abdualrhman, Mohammed Ahmed Ali, and M. C. Padma. "Deterministic Concept Drift Detection in Ensemble Classifier Based Data Stream Classification Process." International Journal of Grid and High Performance Computing 11, no. 1 (2019): 29–48. http://dx.doi.org/10.4018/ijghpc.2019010103.
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The data in streaming environment tends to be non-stationary. Hence, frequent and irregular changes occur in data, which usually denotes as a concept drift related to the process of classifying data streams. Depiction of the concept drift in traditional phase of data stream mining demands availability of labelled samples; however, incorporating the label to a streamlining transaction is infeasible in terms of process time and resource utilization. In this article, deterministic concept drift detection (DCDD) in ensemble classifier-based data stream classification process is proposed, which can depict a concept drift regardless of the labels assigned to samples. The depicted model of DCDD is evaluated by experimental study on dataset called poker-hand. The experimental result showing that the proposed model is accurate and scalable to detect concept drift with high drift detection rate and minimal false alarming and missing rate that compared to other contemporary models.
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Namitha K. and Santhosh Kumar G. "Concept Drift Detection in Data Stream Clustering and its Application on Weather Data." International Journal of Agricultural and Environmental Information Systems 11, no. 1 (2020): 67–85. http://dx.doi.org/10.4018/ijaeis.2020010104.
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This article presents a stream mining framework to cluster the data stream and monitor its evolution. Even though concept drift is expected to be present in data streams, explicit drift detection is rarely done in stream clustering algorithms. The proposed framework is capable of explicit concept drift detection and cluster evolution analysis. Concept drift is caused by the changes in data distribution over time. Relationship between concept drift and the occurrence of physical events has been studied by applying the framework on the weather data stream. Experiments led to the conclusion that the concept drift accompanied by a change in the number of clusters indicates a significant weather event. This kind of online monitoring and its results can be utilized in weather forecasting systems in various ways. Weather data streams produced by automatic weather stations (AWS) are used to conduct this study.
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Manikandaraja, Abishek, Peter Aaby, and Nikolaos Pitropakis. "Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection." Computers 12, no. 10 (2023): 195. http://dx.doi.org/10.3390/computers12100195.
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Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional signature detection, the development of new and novel malware aiming to bypass detection has caused a challenge where models may experience concept drift. However, as new malware samples appear, the detection performance drops. Our work aims to discuss the performance degradation of machine learning-based malware detectors with time, also called concept drift. To achieve this goal, we develop a Python-based framework, namely Rapidrift, capable of analysing the concept drift at a more granular level. We also created two new malware datasets, TRITIUM and INFRENO, from different sources and threat profiles to conduct a deeper analysis of the concept drift problem. To test the effectiveness of Rapidrift, various fundamental methods that could reduce the effects of concept drift were experimentally explored.
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Lin, Ximing, Longtao Chang, Xiushan Nie, and Fei Dong. "Temporal Attention for Few-Shot Concept Drift Detection in Streaming Data." Electronics 13, no. 11 (2024): 2183. http://dx.doi.org/10.3390/electronics13112183.
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Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift is a phenomenon in which the statistical properties of a target domain change over time in an arbitrary way. These changes might be caused by changes in hidden variables that cannot be measured directly. With the onset of the big data era, domains such as social networks, meteorology, and finance are generating copious amounts of streaming data. Embedded within these data, the issue of concept drift can affect the attributes of streaming data in various ways, leading to a decline in the accuracy and performance of models. There is a pressing need for new models to re-adapt to the changes in streaming data. Traditional concept drift detection algorithms struggle to effectively capture and utilize the key feature points of concept drift within complex time series, thereby failing to maintain the accuracy and efficiency of the models. In light of these challenges, this study introduces a novel concept drift detection method that incorporates a temporal attention mechanism within a prototypical network. By integrating a temporal attention mechanism during the feature extraction process, our approach enhances the capability to process complex time series data, preserves temporal locality, strengthens the learning of key features, and reduces the amount of labeled data required. This method significantly improves the detection accuracy and efficiency of small sample streaming data by better capturing the local features of the data. Experiments conducted across multiple datasets demonstrate that this method exhibits comprehensive leading performance in terms of accuracy and F1-score, with excellent recall and precision, thereby validating its effectiveness in enhancing concept drift detection in streaming data.
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Gandhi, Jay, and Vaibhav Gandhi. "Novel Class Detection with Concept Drift in Data Stream - AhtNODE." International Journal of Distributed Systems and Technologies 11, no. 1 (2020): 15–26. http://dx.doi.org/10.4018/ijdst.2020010102.
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Data stream mining has become an interesting analysis topic and it is a growing interest in data discovery method. There are several applications supporting stream data processing like device network, electronic network, etc. Our approach AhtNODE (Adaptive Hoeffding Tree based NOvel class DEtection) detects novel class in the presence of concept drift in streaming data. It addresses there are three challenges of streaming data: infinite length, concept drift, and concept evolution. This approach automatically detects the novel class whenever it arrives in the data stream. It is a multi-class approach that distinguishes novel class from existing classes. The authors tend to apply the Adaptive Hoeffding Tree as a classification model that is also used to handle the concept drift situation. Previous approaches used the ensemble model to handle concept drift. In AHT, classification is done in the single pass. The experiment result proves the effectiveness of AhtNODE compared to existing ensemble classifier in terms of classification accuracy, speed and use of memory.
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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 problem of concept drift detection. With this motivation, we propose a drift detector that reacts naturally to sudden drifts in the absence of class labels. In a novel way, the proposed detector reacts to concept drift in the absence of class labels, where the true label of an example is not necessary. Instead of monitoring the error estimates, the proposed detector monitors the diversity of a pair of classifiers, where the true label of an example is not necessary to determine whether components disagree. Using several datasets, an experimental evaluation and comparison is conducted against several existing detectors. The experiment results show that the proposed detector can detect drifts with less delay, runtime and memory usage.
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Omori, Nicolas Jashchenko, Gabriel Marques Tavares, Paolo Ceravolo, and Sylvio Barbon Jr. "Comparing Concept Drift Detection with Process Mining Software." iSys - Brazilian Journal of Information Systems 13, no. 4 (2020): 101–25. http://dx.doi.org/10.5753/isys.2020.832.
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Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available process mining tools and software that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools, comparing their differences, advantages, and disadvantages by testing against a log taken from a Process Control System. Thus, by highlighting the trade-off between the software, the paper gives the stakeholders the best options regarding their case use.
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Du, L., Q. Song, L. Zhu, and X. Zhu. "A Selective Detector Ensemble for Concept Drift Detection." Computer Journal 58, no. 3 (2014): 457–71. http://dx.doi.org/10.1093/comjnl/bxu050.
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Zambon, Daniele, Cesare Alippi, and Lorenzo Livi. "Concept Drift and Anomaly Detection in Graph Streams." IEEE Transactions on Neural Networks and Learning Systems 29, no. 11 (2018): 5592–605. http://dx.doi.org/10.1109/tnnls.2018.2804443.
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Cabral, Danilo Rafael de Lima, and Roberto Souto Maior de Barros. "Concept drift detection based on Fisher’s Exact test." Information Sciences 442-443 (May 2018): 220–34. http://dx.doi.org/10.1016/j.ins.2018.02.054.
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Adams, Jan Niklas, Cameron Pitsch, Tobias Brockhoff, and Wil M. P. van der Aalst. "An Experimental Evaluation of Process Concept Drift Detection." Proceedings of the VLDB Endowment 16, no. 8 (2023): 1856–69. http://dx.doi.org/10.14778/3594512.3594517.
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Process mining provides techniques to learn models from event data. These models can be descriptive (e.g., Petri nets) or predictive (e.g., neural networks). The learned models offer operational support to process owners by conformance checking, process enhancement, or predictive monitoring. However, processes are frequently subject to significant changes, making the learned models outdated and less valuable over time. To tackle this problem, Process Concept Drift (PCD) detection techniques are employed. By identifying when the process changes occur, one can replace learned models by relearning, updating, or discounting pre-drift knowledge. Various techniques to detect PCDs have been proposed. However, each technique's evaluation focuses on different evaluation goals out of accuracy, latency, versatility, scalability, parameter sensitivity, and robustness. Furthermore, the employed evaluation techniques and data sets differ. Since many techniques are not evaluated against more than one other technique, this lack of comparability raises one question: How do PCD detection techniques compare against each other? With this paper, we propose, implement, and apply a unified evaluation framework for PCD detection. We do this by collecting evaluation goals and evaluation techniques together with data sets. We derive a representative sample of techniques from a taxonomy for PCD detection. The implemented techniques and proposed evaluation framework are provided in a publicly available repository. We present the results of our experimental evaluation and observe that none of the implemented techniques works well across all evaluation goals. However, the results indicate future improvement points of algorithms and guide practitioners.