Artigos de revistas sobre o tema "Concept Drift Detection"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Veja os 50 melhores artigos de revistas para estudos sobre o assunto "Concept Drift Detection".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Veja os artigos de revistas das mais diversas áreas científicas e compile uma bibliografia correta.
Zhu, Jiaqi, Shaofeng Cai, Fang Deng, Beng Chin Ooi e Wenqiao Zhang. "METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection". Proceedings of the VLDB Endowment 17, n.º 4 (dezembro de 2023): 794–807. http://dx.doi.org/10.14778/3636218.3636233.
Texto completo da fonteSakurai, Guilherme Yukio, Jessica Fernandes Lopes, Bruno Bogaz Zarpelão e Sylvio Barbon Junior. "Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives". Future Internet 15, n.º 5 (29 de abril de 2023): 169. http://dx.doi.org/10.3390/fi15050169.
Texto completo da fonteToor, Affan Ahmed, Muhammad Usman, Farah Younas, Alvis Cheuk M. Fong, Sajid Ali Khan e Simon Fong. "Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems". Sensors 20, n.º 7 (9 de abril de 2020): 2131. http://dx.doi.org/10.3390/s20072131.
Texto completo da fonteKumar, Sanjeev, Ravendra Singh, Mohammad Zubair Khan e Abdulfattah Noorwali. "Design of adaptive ensemble classifier for online sentiment analysis and opinion mining". PeerJ Computer Science 7 (5 de agosto de 2021): e660. http://dx.doi.org/10.7717/peerj-cs.660.
Texto completo da fonteDries, Anton, e Ulrich Rückert. "Adaptive concept drift detection". Statistical Analysis and Data Mining: The ASA Data Science Journal 2, n.º 5-6 (18 de novembro de 2009): 311–27. http://dx.doi.org/10.1002/sam.10054.
Texto completo da fontePalli, Abdul Sattar, Jafreezal Jaafar, Heitor Murilo Gomes, Manzoor Ahmed Hashmani e Abdul Rehman Gilal. "An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams". Applied Sciences 12, n.º 22 (17 de novembro de 2022): 11688. http://dx.doi.org/10.3390/app122211688.
Texto completo da fonteHu, Hanqing, e Mehmed Kantardzic. "Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification". Intelligent Decision Technologies 15, n.º 4 (10 de janeiro de 2022): 609–22. http://dx.doi.org/10.3233/idt-210115.
Texto completo da fonteSun, Yange, Zhihai Wang, Yang Bai, Honghua Dai e Saeid Nahavandi. "A Classifier Graph Based Recurring Concept Detection and Prediction Approach". Computational Intelligence and Neuroscience 2018 (7 de junho de 2018): 1–13. http://dx.doi.org/10.1155/2018/4276291.
Texto completo da fonteYOSHIDA, 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 completo da fonteWares, Scott, John Isaacs e Eyad Elyan. "Burst Detection-Based Selective Classifier Resetting". Journal of Information & Knowledge Management 20, n.º 02 (23 de abril de 2021): 2150027. http://dx.doi.org/10.1142/s0219649221500271.
Texto completo da fonteGâlmeanu, Honorius, e Răzvan Andonie. "Concept Drift Adaptation with Incremental–Decremental SVM". Applied Sciences 11, n.º 20 (15 de outubro de 2021): 9644. http://dx.doi.org/10.3390/app11209644.
Texto completo da fonteMcKay, Helen, Nathan Griffiths, Phillip Taylor, Theo Damoulas e Zhou Xu. "Bi-directional online transfer learning: a framework". Annals of Telecommunications 75, n.º 9-10 (outubro de 2020): 523–47. http://dx.doi.org/10.1007/s12243-020-00776-1.
Texto completo da fonteLu, Ning, Guangquan Zhang e Jie Lu. "Concept drift detection via competence models". Artificial Intelligence 209 (abril de 2014): 11–28. http://dx.doi.org/10.1016/j.artint.2014.01.001.
Texto completo da fonteMulimani, Deepa C., Shashikumar G. Totad e Prakashgoud R. Patil. "Concept Drift Adaptation in Intrusion Detection Systems Using Ensemble Learning". International Journal of Natural Computing Research 10, n.º 4 (1 de outubro de 2021): 1–22. http://dx.doi.org/10.4018/ijncr.2021100101.
Texto completo da fonteKumar, Sanjeev, e 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, n.º 06 (29 de junho de 2021): 49–55. http://dx.doi.org/10.51201/jusst/21/06492.
Texto completo da fonteSankara Prasanna Kumar, M., A. P. Siva Kumar e 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, n.º 3.6 (4 de julho de 2018): 148. http://dx.doi.org/10.14419/ijet.v7i3.6.14959.
Texto completo da fonteBarddal, Jean Paul, Heitor Murilo Gomes e Fabrício Enembreck. "Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory". International Journal of Natural Computing Research 5, n.º 1 (janeiro de 2015): 26–41. http://dx.doi.org/10.4018/ijncr.2015010102.
Texto completo da fonteAlthabiti, Mashail Shaeel, e Manal Abdullah. "CDDM: Concept Drift Detection Model for Data Stream". International Journal of Interactive Mobile Technologies (iJIM) 14, n.º 10 (30 de junho de 2020): 90. http://dx.doi.org/10.3991/ijim.v14i10.14803.
Texto completo da fonteSheluhin, Oleg I., Vyacheslav V. Barkov e Airapet G. Simonyan. "Concept drift detection in mobile applications classification using autoencoders". H&ES Research 15, n.º 3 (2023): 20–29. http://dx.doi.org/10.36724/2409-5419-2023-15-3-20-29.
Texto completo da fonteChu, Renjie, Peiyuan Jin, Hanli Qiao e Quanxi Feng. "Intrusion detection in the IoT data streams using concept drift localization". AIMS Mathematics 9, n.º 1 (2023): 1535–61. http://dx.doi.org/10.3934/math.2024076.
Texto completo da fonteLEE, Jeonghoon, e Yoon-Joon LEE. "Concept Drift Detection for Evolving Stream Data". IEICE Transactions on Information and Systems E94-D, n.º 11 (2011): 2288–92. http://dx.doi.org/10.1587/transinf.e94.d.2288.
Texto completo da fonteBeshah, Yonas Kibret, Surafel Lemma Abebe e Henock Mulugeta Melaku. "Drift Adaptive Online DDoS Attack Detection Framework for IoT System". Electronics 13, n.º 6 (7 de março de 2024): 1004. http://dx.doi.org/10.3390/electronics13061004.
Texto completo da fonteDesale, Ketan Sanjay, e Swati Shinde. "Real-Time Concept Drift Detection and Its Application to ECG Data". International Journal of Online and Biomedical Engineering (iJOE) 17, n.º 10 (19 de outubro de 2021): 160. http://dx.doi.org/10.3991/ijoe.v17i10.25473.
Texto completo da fonteMehmood, Tajwar, Seemab Latif, Nor Shahida Mohd Jamail, Asad Malik e Rabia Latif. "LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing". PeerJ Computer Science 10 (31 de janeiro de 2024): e1827. http://dx.doi.org/10.7717/peerj-cs.1827.
Texto completo da fonteSubha, S., e 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, n.º 5 (31 de janeiro de 2024): 386–96. http://dx.doi.org/10.17485/ijst/v17i5.1645.
Texto completo da fonteAbdualrhman, Mohammed Ahmed Ali, e M. C. Padma. "Deterministic Concept Drift Detection in Ensemble Classifier Based Data Stream Classification Process". International Journal of Grid and High Performance Computing 11, n.º 1 (janeiro de 2019): 29–48. http://dx.doi.org/10.4018/ijghpc.2019010103.
Texto completo da fonteAdebayo, Oluwadare Samuel, Thompson Aderonke Favour-Bethy, Owolafe Otasowie e 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, n.º 7 (30 de julho de 2023): 24–48. http://dx.doi.org/10.47760/ijcsmc.2023.v12i07.004.
Texto completo da fonteManikandaraja, Abishek, Peter Aaby e Nikolaos Pitropakis. "Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection". Computers 12, n.º 10 (28 de setembro de 2023): 195. http://dx.doi.org/10.3390/computers12100195.
Texto completo da fonteNamitha K. e 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, n.º 1 (janeiro de 2020): 67–85. http://dx.doi.org/10.4018/ijaeis.2020010104.
Texto completo da fonteLi, Xiangjun, Yong Zhou, Ziyan Jin, Peng Yu e 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 (19 de março de 2020): 1–8. http://dx.doi.org/10.1155/2020/4027423.
Texto completo da fonteOmori, Nicolas Jashchenko, Gabriel Marques Tavares, Paolo Ceravolo e Sylvio Barbon Jr. "Comparing Concept Drift Detection with Process Mining Software". iSys - Brazilian Journal of Information Systems 13, n.º 4 (31 de julho de 2020): 101–25. http://dx.doi.org/10.5753/isys.2020.832.
Texto completo da fonteDu, L., Q. Song, L. Zhu e X. Zhu. "A Selective Detector Ensemble for Concept Drift Detection". Computer Journal 58, n.º 3 (20 de junho de 2014): 457–71. http://dx.doi.org/10.1093/comjnl/bxu050.
Texto completo da fonteZambon, Daniele, Cesare Alippi e Lorenzo Livi. "Concept Drift and Anomaly Detection in Graph Streams". IEEE Transactions on Neural Networks and Learning Systems 29, n.º 11 (novembro de 2018): 5592–605. http://dx.doi.org/10.1109/tnnls.2018.2804443.
Texto completo da fonteCabral, Danilo Rafael de Lima, e Roberto Souto Maior de Barros. "Concept drift detection based on Fisher’s Exact test". Information Sciences 442-443 (maio de 2018): 220–34. http://dx.doi.org/10.1016/j.ins.2018.02.054.
Texto completo da fonteAdams, Jan Niklas, Cameron Pitsch, Tobias Brockhoff e Wil M. P. van der Aalst. "An Experimental Evaluation of Process Concept Drift Detection". Proceedings of the VLDB Endowment 16, n.º 8 (abril de 2023): 1856–69. http://dx.doi.org/10.14778/3594512.3594517.
Texto completo da fonteSun, Yingying, Jusheng Mi e Chenxia Jin. "Entropy-based concept drift detection in information systems". Knowledge-Based Systems 290 (abril de 2024): 111596. http://dx.doi.org/10.1016/j.knosys.2024.111596.
Texto completo da fonteGandhi, Jay, e Vaibhav Gandhi. "Novel Class Detection with Concept Drift in Data Stream - AhtNODE". International Journal of Distributed Systems and Technologies 11, n.º 1 (janeiro de 2020): 15–26. http://dx.doi.org/10.4018/ijdst.2020010102.
Texto completo da fonteMahdi, Osama A., Eric Pardede, Nawfal Ali e Jinli Cao. "Fast Reaction to Sudden Concept Drift in the Absence of Class Labels". Applied Sciences 10, n.º 2 (14 de janeiro de 2020): 606. http://dx.doi.org/10.3390/app10020606.
Texto completo da fontePalli, Abdul Sattar, Jafreezal Jaafar, Abdul Rehman Gilal, Aeshah Alsughayyir, Heitor Murilo Gomes, Abdullah Alshanqiti e 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, n.º 1 (30 de janeiro de 2024): 105–39. http://dx.doi.org/10.32890/jict2024.23.1.5.
Texto completo da fonteSato, Denise Maria Vecino, Sheila Cristiana De Freitas, Jean Paul Barddal e Edson Emilio Scalabrin. "A Survey on Concept Drift in Process Mining". ACM Computing Surveys 54, n.º 9 (31 de dezembro de 2022): 1–38. http://dx.doi.org/10.1145/3472752.
Texto completo da fonteVyawhare, Chaitanya R., Reshma Y. Totare, Prashant S. Sonawane e Purva B. Deshmukh. "Machine Learning System for Malicious Website Detection using Concept Drift Detection". International Journal for Research in Applied Science and Engineering Technology 10, n.º 5 (31 de maio de 2022): 47–55. http://dx.doi.org/10.22214/ijraset.2022.42048.
Texto completo da fonteElkhawaga, Ghada, Mervat Abuelkheir, Sherif I. Barakat, Alaa M. Riad e Manfred Reichert. "CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining". Algorithms 13, n.º 7 (3 de julho de 2020): 161. http://dx.doi.org/10.3390/a13070161.
Texto completo da fonteHenke, Marcia, Eulanda Santos, Eduardo Souto e Altair O. Santin. "Spam Detection Based on Feature Evolution to Deal with Concept Drift". JUCS - Journal of Universal Computer Science 27, n.º 4 (28 de abril de 2021): 364–86. http://dx.doi.org/10.3897/jucs.66284.
Texto completo da fonteYang, Rui, Shuliang Xu e Lin Feng. "An Ensemble Extreme Learning Machine for Data Stream Classification". Algorithms 11, n.º 7 (17 de julho de 2018): 107. http://dx.doi.org/10.3390/a11070107.
Texto completo da fonteChen, Xue, Yang Song, Wei Xiong, Yutao Lu e Xingen Wang. "Research on Web Robot Detection Technology for Concept Drift". Journal of Physics: Conference Series 2010, n.º 1 (1 de setembro de 2021): 012161. http://dx.doi.org/10.1088/1742-6596/2010/1/012161.
Texto completo da fonteMiyata, Yasushi, e Hiroshi Ishikawa. "Concept Drift Detection on Stream Data for Revising DBSCAN". IEEJ Transactions on Electronics, Information and Systems 140, n.º 8 (1 de agosto de 2020): 949–55. http://dx.doi.org/10.1541/ieejeiss.140.949.
Texto completo da fonteCejnek, Matous, e Ivo Bukovsky. "Concept drift robust adaptive novelty detection for data streams". Neurocomputing 309 (outubro de 2018): 46–53. http://dx.doi.org/10.1016/j.neucom.2018.04.069.
Texto completo da fonteEscovedo, Tatiana, Adriano Koshiyama, Andre Abs da Cruz e Marley Vellasco. "DetectA: abrupt concept drift detection in non-stationary environments". Applied Soft Computing 62 (janeiro de 2018): 119–33. http://dx.doi.org/10.1016/j.asoc.2017.10.031.
Texto completo da fonteZenisek, Jan, Florian Holzinger e Michael Affenzeller. "Machine learning based concept drift detection for predictive maintenance". Computers & Industrial Engineering 137 (novembro de 2019): 106031. http://dx.doi.org/10.1016/j.cie.2019.106031.
Texto completo da fonteYu, Shujian, Zubin Abraham, Heng Wang, Mohak Shah, Yantao Wei e José C. Príncipe. "Concept drift detection and adaptation with hierarchical hypothesis testing". Journal of the Franklin Institute 356, n.º 5 (março de 2019): 3187–215. http://dx.doi.org/10.1016/j.jfranklin.2019.01.043.
Texto completo da fonte