Journal articles on the topic 'Concept drift'
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Museba, Tinofirei, Fulufhelo Nelwamondo, and Khmaies Ouahada. "ADES: A New Ensemble Diversity-Based Approach for Handling Concept Drift." Mobile Information Systems 2021 (June 1, 2021): 1–17. http://dx.doi.org/10.1155/2021/5549300.
Full textZhu, 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 (December 2023): 794–807. http://dx.doi.org/10.14778/3636218.3636233.
Full textSakurai, 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 (April 29, 2023): 169. http://dx.doi.org/10.3390/fi15050169.
Full textToor, 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 (April 9, 2020): 2131. http://dx.doi.org/10.3390/s20072131.
Full textYao, Yuan. "Concept Drift Visualization." Journal of Information and Computational Science 10, no. 10 (July 1, 2013): 3021–29. http://dx.doi.org/10.12733/jics20101915.
Full textWebb, Geoffrey I., Roy Hyde, Hong Cao, Hai Long Nguyen, and Francois Petitjean. "Characterizing concept drift." Data Mining and Knowledge Discovery 30, no. 4 (April 15, 2016): 964–94. http://dx.doi.org/10.1007/s10618-015-0448-4.
Full textYang, Lingkai, Sally McClean, Mark Donnelly, Kevin Burke, and Kashaf Khan. "Detecting and Responding to Concept Drift in Business Processes." Algorithms 15, no. 5 (May 21, 2022): 174. http://dx.doi.org/10.3390/a15050174.
Full textSun, 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.
Full textDries, Anton, and Ulrich Rückert. "Adaptive concept drift detection." Statistical Analysis and Data Mining: The ASA Data Science Journal 2, no. 5-6 (November 18, 2009): 311–27. http://dx.doi.org/10.1002/sam.10054.
Full textOrtíz Díaz, Agustín, José del Campo-Ávila, Gonzalo Ramos-Jiménez, Isvani Frías Blanco, Yailé Caballero Mota, Antonio Mustelier Hechavarría, and 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.
Full textSankara 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 (July 4, 2018): 148. http://dx.doi.org/10.14419/ijet.v7i3.6.14959.
Full textMuhammad Zaly Shah, Muhammad Zafran, Anazida Zainal, Taiseer Abdalla Elfadil Eisa, Hashim Albasheer, and Fuad A. Ghaleb. "A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer." Mathematics 11, no. 2 (January 9, 2023): 355. http://dx.doi.org/10.3390/math11020355.
Full textMahdi, 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 (January 14, 2020): 606. http://dx.doi.org/10.3390/app10020606.
Full textMuseba, Tinofirei, Fulufhelo Nelwamondo, Khmaies Ouahada, and Ayokunle Akinola. "Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts." Applied Computational Intelligence and Soft Computing 2021 (June 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/5533777.
Full textMcKay, Helen, Nathan Griffiths, Phillip Taylor, Theo Damoulas, and Zhou Xu. "Bi-directional online transfer learning: a framework." Annals of Telecommunications 75, no. 9-10 (October 2020): 523–47. http://dx.doi.org/10.1007/s12243-020-00776-1.
Full textWares, Scott, John Isaacs, and Eyad Elyan. "Burst Detection-Based Selective Classifier Resetting." Journal of Information & Knowledge Management 20, no. 02 (April 23, 2021): 2150027. http://dx.doi.org/10.1142/s0219649221500271.
Full textBabko-Malyi, Sergei. "Ion-drift reactor™ concept." Fuel Processing Technology 65-66 (June 2000): 231–46. http://dx.doi.org/10.1016/s0378-3820(99)00100-9.
Full textYOSHIDA, Kenichi. "Brute force concept drift detection." Procedia Computer Science 225 (2023): 1672–81. http://dx.doi.org/10.1016/j.procs.2023.10.156.
Full textHewahi, Nabil M., and Ibrahim M. Elbouhissi. "Concepts Seeds Gathering and Dataset Updating Algorithm for Handling Concept Drift." International Journal of Decision Support System Technology 7, no. 2 (April 2015): 29–57. http://dx.doi.org/10.4018/ijdsst.2015040103.
Full textMulimani, 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 (October 1, 2021): 1–22. http://dx.doi.org/10.4018/ijncr.2021100101.
Full textPalli, 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 (November 17, 2022): 11688. http://dx.doi.org/10.3390/app122211688.
Full textJu, Chun Hua, and Li Li Mao. "Decision Tree Classification Algorithm within Concept Similarity." Applied Mechanics and Materials 235 (November 2012): 9–14. http://dx.doi.org/10.4028/www.scientific.net/amm.235.9.
Full textBarddal, 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 (January 2015): 26–41. http://dx.doi.org/10.4018/ijncr.2015010102.
Full textGâlmeanu, Honorius, and Răzvan Andonie. "Concept Drift Adaptation with Incremental–Decremental SVM." Applied Sciences 11, no. 20 (October 15, 2021): 9644. http://dx.doi.org/10.3390/app11209644.
Full textLiu, Shinan, Francesco Bronzino, Paul Schmitt, Arjun Nitin Bhagoji, Nick Feamster, Hector Garcia Crespo, Timothy Coyle, and Brian Ward. "LEAF: Navigating Concept Drift in Cellular Networks." Proceedings of the ACM on Networking 1, no. 2 (September 28, 2023): 1–24. http://dx.doi.org/10.1145/3609422.
Full textHu, 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 (January 10, 2022): 609–22. http://dx.doi.org/10.3233/idt-210115.
Full textKim, Minsu, Seong-Hyeon Hwang, and Steven Euijong Whang. "Quilt: Robust Data Segment Selection against Concept Drifts." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (March 24, 2024): 21249–57. http://dx.doi.org/10.1609/aaai.v38i19.30119.
Full textKumar, 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.
Full textThangam, M., A. Bhuvaneswari, and J. Sangeetha. "A Framework to Detect and Classify Time-based Concept Drift." Indian Journal Of Science And Technology 16, no. 48 (December 28, 2023): 4631–37. http://dx.doi.org/10.17485/ijst/v16i48.583.
Full textBudiman, Arif, Mohamad Ivan Fanany, and Chan Basaruddin. "Adaptive Online Sequential ELM for Concept Drift Tackling." Computational Intelligence and Neuroscience 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/8091267.
Full textAlthabiti, Mashail, and Manal Abdullah*. "Streaming Data Classification With Concept Drift." Bioscience Biotechnology Research Communications 12, no. 1 (February 25, 2019): 177–84. http://dx.doi.org/10.21786/bbrc/12.1/20.
Full textIwashita, Adriana Sayuri, and Joao Paulo Papa. "An Overview on Concept Drift Learning." IEEE Access 7 (2019): 1532–47. http://dx.doi.org/10.1109/access.2018.2886026.
Full textGama, João, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. "A survey on concept drift adaptation." ACM Computing Surveys 46, no. 4 (April 2014): 1–37. http://dx.doi.org/10.1145/2523813.
Full textCase, John, Sanjay Jain, Susanne Kaufmann, Arun Sharma, and Frank Stephan. "Predictive learning models for concept drift." Theoretical Computer Science 268, no. 2 (October 2001): 323–49. http://dx.doi.org/10.1016/s0304-3975(00)00274-7.
Full textGonçalves Jr, Paulo Mauricio, and Roberto Souto Maior de Barros. "RCD: A recurring concept drift framework." Pattern Recognition Letters 34, no. 9 (July 2013): 1018–25. http://dx.doi.org/10.1016/j.patrec.2013.02.005.
Full textLifna, C. S., and M. Vijayalakshmi. "Identifying Concept-drift in Twitter Streams." Procedia Computer Science 45 (2015): 86–94. http://dx.doi.org/10.1016/j.procs.2015.03.093.
Full textLu, 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.
Full textZhao, Peng, Le-Wen Cai, and Zhi-Hua Zhou. "Handling concept drift via model reuse." Machine Learning 109, no. 3 (October 10, 2019): 533–68. http://dx.doi.org/10.1007/s10994-019-05835-w.
Full textDesale, 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 (October 19, 2021): 160. http://dx.doi.org/10.3991/ijoe.v17i10.25473.
Full textPalli, Abdul Sattar, Jafreezal Jaafar, Abdul Rehman Gilal, Aeshah Alsughayyir, Heitor Murilo Gomes, Abdullah Alshanqiti, and 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, no. 1 (January 30, 2024): 105–39. http://dx.doi.org/10.32890/jict2024.23.1.5.
Full textSato, Denise Maria Vecino, Sheila Cristiana De Freitas, Jean Paul Barddal, and Edson Emilio Scalabrin. "A Survey on Concept Drift in Process Mining." ACM Computing Surveys 54, no. 9 (December 31, 2022): 1–38. http://dx.doi.org/10.1145/3472752.
Full textYang, Rui, Shuliang Xu, and Lin Feng. "An Ensemble Extreme Learning Machine for Data Stream Classification." Algorithms 11, no. 7 (July 17, 2018): 107. http://dx.doi.org/10.3390/a11070107.
Full textChu, 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.
Full textSheluhin, 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.
Full textAlthabiti, Mashail Shaeel, and Manal Abdullah. "CDDM: Concept Drift Detection Model for Data Stream." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 10 (June 30, 2020): 90. http://dx.doi.org/10.3991/ijim.v14i10.14803.
Full textXiang, Qiuyan, Lingling Zi, Xin Cong, and Yan Wang. "Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review." Applied Sciences 13, no. 11 (May 26, 2023): 6515. http://dx.doi.org/10.3390/app13116515.
Full textNamitha 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 (January 2020): 67–85. http://dx.doi.org/10.4018/ijaeis.2020010104.
Full textAbdualrhman, 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 (January 2019): 29–48. http://dx.doi.org/10.4018/ijghpc.2019010103.
Full textESCOVEDO, TATIANA, ANDRÉ V. ABS DA CRUZ, MARLEY M. B. R. VELLASCO, and ADRIANO S. KOSHIYAMA. "LEARNING UNDER CONCEPT DRIFT USING A NEURO-EVOLUTIONARY ENSEMBLE." International Journal of Computational Intelligence and Applications 12, no. 04 (December 2013): 1340002. http://dx.doi.org/10.1142/s1469026813400026.
Full textElkhawaga, Ghada, Mervat Abuelkheir, Sherif I. Barakat, Alaa M. Riad, and Manfred Reichert. "CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining." Algorithms 13, no. 7 (July 3, 2020): 161. http://dx.doi.org/10.3390/a13070161.
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