Artículos de revistas sobre el tema "Concept Drift Detection"
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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.
Texto completoSakurai, 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.
Texto completoToor, 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.
Texto completoKumar, 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.
Texto completoM, 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.
Texto completoDries, 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.
Texto completoLu, 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.
Texto completoPalli, 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.
Texto completoHu, 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.
Texto completoSobolewski, 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.
Texto completoSun, 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.
Texto completoYOSHIDA, Kenichi. "Brute force concept drift detection." Procedia Computer Science 225 (2023): 1672–81. http://dx.doi.org/10.1016/j.procs.2023.10.156.
Texto completoWares, 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.
Texto completoGâlmeanu, 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.
Texto completoMcKay, 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.
Texto completoLu, 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.
Texto completoMulimani, 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.
Texto completoKumar, 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.
Texto completoHan, 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.
Texto completoSankara 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.
Texto completoBarddal, 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.
Texto completoAlthabiti, 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.
Texto completoChu, 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.
Texto completoGower-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.
Texto completoS, 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.
Texto completoCosta, 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.
Texto completoSheluhin, 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.
Texto completoSandeep 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.
Texto completoSubha, 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.
Texto completoLEE, 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.
Texto completoDesale, 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.
Texto completoMehmood, 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.
Texto completoBeshah, 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.
Texto completoMauricio 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.
Texto completoVasilieva, 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.
Texto completoPriyanka 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.
Texto completoHu, 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.
Texto completoAdebayo, 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.
Texto completoLi, 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.
Texto completoAbdualrhman, 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.
Texto completoNamitha 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.
Texto completoManikandaraja, 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.
Texto completoLin, 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.
Texto completoGandhi, 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.
Texto completoMahdi, 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.
Texto completoOmori, 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.
Texto completoDu, 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.
Texto completoZambon, 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.
Texto completoCabral, 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.
Texto completoAdams, 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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