Artykuły w czasopismach: „Concept drift”

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Museba, Tinofirei, Fulufhelo Nelwamondo i Khmaies Ouahada. "ADES: A New Ensemble Diversity-Based Approach for Handling Concept Drift". Mobile Information Systems 2021 (1.06.2021): 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 often evolve with time, a phenomenon known as concept drift. Most work in handling concept drift focuses on updating the prediction model so that it can recover from concept drift while little effort has been dedicated to the formulation of a learning system that is capable of learning different types of drifting concepts at any time with minimum overheads. This work proposes a novel and evolving data stream classifier called Adaptive Diversified Ensemble Selection Classifier (ADES) that significantly optimizes adaptation to different types of concept drifts at any time and improves convergence to new concepts by exploiting different amounts of ensemble diversity. The ADES algorithm generates diverse base classifiers, thereby optimizing the margin distribution to exploit ensemble diversity to formulate an ensemble classifier that generalizes well to unseen instances and provides fast recovery from different types of concept drift. Empirical experiments conducted on both artificial and real-world data streams demonstrate that ADES can adapt to different types of drifts at any given time. The prediction performance of ADES is compared to three other ensemble classifiers designed to handle concept drift using both artificial and real-world data streams. The comparative evaluation performed demonstrated the ability of ADES to handle different types of concept drifts. The experimental results, including statistical test results, indicate comparable performances with other algorithms designed to handle concept drift and prove their significance and effectiveness.

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Zhu, Jiaqi, Shaofeng Cai, Fang Deng, Beng Chin Ooi i Wenqiao Zhang. "METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection". Proceedings of the VLDB Endowment 17, nr 4 (grudzień 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 i Sylvio Barbon Junior. "Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives". Future Internet 15, nr 5 (29.04.2023): 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 i Simon Fong. "Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems". Sensors 20, nr 7 (9.04.2020): 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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Yao, Yuan. "Concept Drift Visualization". Journal of Information and Computational Science 10, nr 10 (1.07.2013): 3021–29. http://dx.doi.org/10.12733/jics20101915.

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Webb, Geoffrey I., Roy Hyde, Hong Cao, Hai Long Nguyen i Francois Petitjean. "Characterizing concept drift". Data Mining and Knowledge Discovery 30, nr 4 (15.04.2016): 964–94. http://dx.doi.org/10.1007/s10618-015-0448-4.

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Yang, Lingkai, Sally McClean, Mark Donnelly, Kevin Burke i Kashaf Khan. "Detecting and Responding to Concept Drift in Business Processes". Algorithms 15, nr 5 (21.05.2022): 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 (gradual drift) or maintain the model as unchanged (no drift). In this paper, we model a response to concept drift as a sequential decision making problem by combing a hierarchical Markov model and a Markov decision process (MDP). The approach can detect concept drift, retrain the model and update customer profiles automatically. We validate the proposed approach on 68 artificial datasets and a real-world hospital billing dataset, with experimental results showing promising performance.

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Sun, Yange, Zhihai Wang, Yang Bai, Honghua Dai i Saeid Nahavandi. "A Classifier Graph Based Recurring Concept Detection and Prediction Approach". Computational Intelligence and Neuroscience 2018 (7.06.2018): 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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Dries, Anton, i Ulrich Rückert. "Adaptive concept drift detection". Statistical Analysis and Data Mining: The ASA Data Science Journal 2, nr 5-6 (18.11.2009): 311–27. http://dx.doi.org/10.1002/sam.10054.

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Ortíz Díaz, Agustín, José del Campo-Ávila, Gonzalo Ramos-Jiménez, Isvani Frías Blanco, Yailé Caballero Mota, Antonio Mustelier Hechavarría i Rafael Morales-Bueno. "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 mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts.

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Sankara Prasanna Kumar, M., A. P. Siva Kumar i 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, nr 3.6 (4.07.2018): 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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Muhammad Zaly Shah, Muhammad Zafran, Anazida Zainal, Taiseer Abdalla Elfadil Eisa, Hashim Albasheer i Fuad A. Ghaleb. "A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer". Mathematics 11, nr 2 (9.01.2023): 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 approaches to adapt to environmental changes assume that the data stream is fully labeled. Assuming a fully labeled data stream is a flawed assumption as the labeling effort would be too impractical due to the rapid arrival and volume of the data. To address this issue, this study proposes to detect concept drift by anticipating a possible change in the true label in the high confidence prediction region. This study also proposes an ensemble-based concept drift adaptation approach that transfers reliable classifiers to the new concept. The significance of our proposed approach compared to the current baselines is that our approach does not use a performance measur as the drift signal or assume a change in data distribution when concept drift occurs. As a result, our proposed approach can detect concept drift when labeled data are scarce, even when the data distribution remains static. Based on the results, this proposed approach can detect concept drifts and fully supervised data stream mining approaches and performs well on mixed-severity concept drift datasets.

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Mahdi, Osama A., Eric Pardede, Nawfal Ali i Jinli Cao. "Fast Reaction to Sudden Concept Drift in the Absence of Class Labels". Applied Sciences 10, nr 2 (14.01.2020): 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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Museba, Tinofirei, Fulufhelo Nelwamondo, Khmaies Ouahada i Ayokunle Akinola. "Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts". Applied Computational Intelligence and Soft Computing 2021 (10.06.2021): 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 the loss of information if the concept was well learned by the learning model, and the concept will recur again in the next learning phase. A common remedy for most machine learning algorithms is to retain and reuse previously learned models, but the process is time-consuming and computationally prohibitive in nonstationary environments to appropriately select any optimal ensemble classifier capable of accurately adapting to recurring concepts. To learn streaming data, fast and accurate machine learning algorithms are needed for time-dependent applications. Most of the existing algorithms designed to handle concept drift do not take into account the presence of recurring concept drift. To accurately and efficiently handle recurring concepts with minimum computational overheads, we propose a novel and evolving ensemble method called Recurrent Adaptive Classifier Ensemble (RACE). The algorithm preserves an archive of previously learned models that are diverse and always trains both new and existing classifiers. The empirical experiments conducted on synthetic and real-world data stream benchmarks show that RACE significantly adapts to recurring concepts more accurately than some state-of-the-art ensemble classifiers based on classifier reuse.

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McKay, Helen, Nathan Griffiths, Phillip Taylor, Theo Damoulas i Zhou Xu. "Bi-directional online transfer learning: a framework". Annals of Telecommunications 75, nr 9-10 (październik 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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Wares, Scott, John Isaacs i Eyad Elyan. "Burst Detection-Based Selective Classifier Resetting". Journal of Information & Knowledge Management 20, nr 02 (23.04.2021): 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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Babko-Malyi, Sergei. "Ion-drift reactor™ concept". Fuel Processing Technology 65-66 (czerwiec 2000): 231–46. http://dx.doi.org/10.1016/s0378-3820(99)00100-9.

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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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Hewahi, Nabil M., i Ibrahim M. Elbouhissi. "Concepts Seeds Gathering and Dataset Updating Algorithm for Handling Concept Drift". International Journal of Decision Support System Technology 7, nr 2 (kwiecień 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 datasets. The experiments conducted show that after applying the authors' approach, the classification accuracy increased from low values to high and acceptable ones. Finally, a comparison study between CSG-DU and Set Formation for Delayed Labeling algorithm (SFDL) has been conducted; SFDL is an approach that handles sudden and gradual concept drift. CSG-DU results outperforms SFDL in terms of classification accuracy.

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Mulimani, Deepa C., Shashikumar G. Totad i Prakashgoud R. Patil. "Concept Drift Adaptation in Intrusion Detection Systems Using Ensemble Learning". International Journal of Natural Computing Research 10, nr 4 (1.10.2021): 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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Palli, Abdul Sattar, Jafreezal Jaafar, Heitor Murilo Gomes, Manzoor Ahmed Hashmani i Abdul Rehman Gilal. "An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams". Applied Sciences 12, nr 22 (17.11.2022): 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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Ju, Chun Hua, i Li Li Mao. "Decision Tree Classification Algorithm within Concept Similarity". Applied Mechanics and Materials 235 (listopad 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 don’t need training any more. Meanwhile, it reduces the cost of update model, speeds up the classification of the rate and improves the prediction efficiency.

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Barddal, Jean Paul, Heitor Murilo Gomes i Fabrício Enembreck. "Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory". International Journal of Natural Computing Research 5, nr 1 (styczeń 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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Gâlmeanu, Honorius, i Răzvan Andonie. "Concept Drift Adaptation with Incremental–Decremental SVM". Applied Sciences 11, nr 20 (15.10.2021): 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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Liu, Shinan, Francesco Bronzino, Paul Schmitt, Arjun Nitin Bhagoji, Nick Feamster, Hector Garcia Crespo, Timothy Coyle i Brian Ward. "LEAF: Navigating Concept Drift in Cellular Networks". Proceedings of the ACM on Networking 1, nr 2 (28.09.2023): 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 metropolitan area in the United States. We find that concept drift occurs across many important key performance indicators (KPIs), independently of the model, training set size, and time interval---thus necessitating practical approaches to detect, explain, and mitigate it. We then show that frequent model retraining with newly available data is not sufficient to mitigate concept drift, and can even degrade model accuracy further. Finally, we develop a new methodology for concept drift mitigation, Local Error Approximation of Features (LEAF). LEAF works by detecting drift; explaining the features and time intervals that contribute the most to drift; and mitigates it using forgetting and over-sampling. We evaluate LEAF against industry-standard mitigation approaches (notably, periodic retraining) with more than four years of cellular KPI data. Our initial tests with a major cellular provider in the US show that LEAF consistently outperforms periodic and triggered retraining on complex, real-world data while reducing costly retraining operations.

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Hu, Hanqing, i Mehmed Kantardzic. "Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification". Intelligent Decision Technologies 15, nr 4 (10.01.2022): 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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Kim, Minsu, Seong-Hyeon Hwang i Steven Euijong Whang. "Quilt: Robust Data Segment Selection against Concept Drifts". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 19 (24.03.2024): 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 accuracy and propose Quilt, a data-centric framework for identifying and selecting data segments that maximize model accuracy. To address the potential downside of efficiency, Quilt extends existing data subset selection techniques, which can be used to reduce the training data without compromising model accuracy. These techniques cannot be used as is because they only assume virtual drifts where the posterior probabilities P(y|X) are assumed not to change. In contrast, a key challenge in our setup is to also discard undesirable data segments with concept drifts. Quilt thus discards drifted data segments and selects data segment subsets holistically for accurate and efficient model training. The two operations use gradient-based scores, which have little computation overhead. In our experiments, we show that Quilt outperforms state-of-the-art drift adaptation and data selection baselines on synthetic and real datasets.

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Kumar, Sanjeev, Ravendra Singh, Mohammad Zubair Khan i Abdulfattah Noorwali. "Design of adaptive ensemble classifier for online sentiment analysis and opinion mining". PeerJ Computer Science 7 (5.08.2021): 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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Thangam, M., A. Bhuvaneswari i J. Sangeetha. "A Framework to Detect and Classify Time-based Concept Drift". Indian Journal Of Science And Technology 16, nr 48 (28.12.2023): 4631–37. http://dx.doi.org/10.17485/ijst/v16i48.583.

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Budiman, Arif, Mohamad Ivan Fanany i 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 type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect “underfitting” condition.

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Althabiti, Mashail, i Manal Abdullah*. "Streaming Data Classification With Concept Drift". Bioscience Biotechnology Research Communications 12, nr 1 (25.02.2019): 177–84. http://dx.doi.org/10.21786/bbrc/12.1/20.

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Iwashita, Adriana Sayuri, i 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 i Abdelhamid Bouchachia. "A survey on concept drift adaptation". ACM Computing Surveys 46, nr 4 (kwiecień 2014): 1–37. http://dx.doi.org/10.1145/2523813.

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Case, John, Sanjay Jain, Susanne Kaufmann, Arun Sharma i Frank Stephan. "Predictive learning models for concept drift". Theoretical Computer Science 268, nr 2 (październik 2001): 323–49. http://dx.doi.org/10.1016/s0304-3975(00)00274-7.

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Gonçalves Jr, Paulo Mauricio, i Roberto Souto Maior de Barros. "RCD: A recurring concept drift framework". Pattern Recognition Letters 34, nr 9 (lipiec 2013): 1018–25. http://dx.doi.org/10.1016/j.patrec.2013.02.005.

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Lifna, C. S., i 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 i Jie Lu. "Concept drift detection via competence models". Artificial Intelligence 209 (kwiecień 2014): 11–28. http://dx.doi.org/10.1016/j.artint.2014.01.001.

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Zhao, Peng, Le-Wen Cai i Zhi-Hua Zhou. "Handling concept drift via model reuse". Machine Learning 109, nr 3 (10.10.2019): 533–68. http://dx.doi.org/10.1007/s10994-019-05835-w.

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Desale, Ketan Sanjay, i Swati Shinde. "Real-Time Concept Drift Detection and Its Application to ECG Data". International Journal of Online and Biomedical Engineering (iJOE) 17, nr 10 (19.10.2021): 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 & time-series datasets.

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Palli, Abdul Sattar, Jafreezal Jaafar, Abdul Rehman Gilal, Aeshah Alsughayyir, Heitor Murilo Gomes, Abdullah Alshanqiti i Mazni Omar. "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, nr 1 (30.01.2024): 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 classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from nonstationarydata streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance alongwith concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions.

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Sato, Denise Maria Vecino, Sheila Cristiana De Freitas, Jean Paul Barddal i Edson Emilio Scalabrin. "A Survey on Concept Drift in Process Mining". ACM Computing Surveys 54, nr 9 (31.12.2022): 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, datasets, and metrics.

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Yang, Rui, Shuliang Xu i Lin Feng. "An Ensemble Extreme Learning Machine for Data Stream Classification". Algorithms 11, nr 7 (17.07.2018): 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 results showed the new ELM algorithm not only can improve the accuracy of classification result, but also can adapt to new concept in a short time.

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Chu, Renjie, Peiyuan Jin, Hanli Qiao i Quanxi Feng. "Intrusion detection in the IoT data streams using concept drift localization". AIMS Mathematics 9, nr 1 (2023): 1535–61. http://dx.doi.org/10.3934/math.2024076.

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<abstract>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.</abstract>

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Sheluhin, Oleg I., Vyacheslav V. Barkov i Airapet G. Simonyan. "Concept drift detection in mobile applications classification using autoencoders". H&ES Research 15, nr 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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Althabiti, Mashail Shaeel, i Manal Abdullah. "CDDM: Concept Drift Detection Model for Data Stream". International Journal of Interactive Mobile Technologies (iJIM) 14, nr 10 (30.06.2020): 90. http://dx.doi.org/10.3991/ijim.v14i10.14803.

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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.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.

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Xiang, Qiuyan, Lingling Zi, Xin Cong i Yan Wang. "Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review". Applied Sciences 13, nr 11 (26.05.2023): 6515. http://dx.doi.org/10.3390/app13116515.

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With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

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Namitha K. i 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, nr 1 (styczeń 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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Abdualrhman, Mohammed Ahmed Ali, i M. C. Padma. "Deterministic Concept Drift Detection in Ensemble Classifier Based Data Stream Classification Process". International Journal of Grid and High Performance Computing 11, nr 1 (styczeń 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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ESCOVEDO, TATIANA, ANDRÉ V. ABS DA CRUZ, MARLEY M. B. R. VELLASCO i ADRIANO S. KOSHIYAMA. "LEARNING UNDER CONCEPT DRIFT USING A NEURO-EVOLUTIONARY ENSEMBLE". International Journal of Computational Intelligence and Applications 12, nr 04 (grudzień 2013): 1340002. http://dx.doi.org/10.1142/s1469026813400026.

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This work describes the use of a weighted ensemble of neural network classifiers for adaptive learning. We train the neural networks by means of a quantum-inspired evolutionary algorithm (QIEA). The QIEA is also used to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. After running several simulations using two different datasets and performing two different analysis of the results, we show that the proposed algorithm, named neuro-evolutionary ensemble (NEVE), was able to learn the data set and to quickly respond to any drifts on the underlying data, indicating that our model can be a good alternative to address concept drift problems. We also compare the results obtained by our model with an existing algorithm, Learn++.NSE, in two different nonstationary scenarios.

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Elkhawaga, Ghada, Mervat Abuelkheir, Sherif I. Barakat, Alaa M. Riad i Manfred Reichert. "CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining". Algorithms 13, nr 7 (3.07.2020): 161. http://dx.doi.org/10.3390/a13070161.

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Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

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