Articoli di riviste sul tema "Concept Drift Detection"
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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. 4 (dicembre 2023): 794–807. http://dx.doi.org/10.14778/3636218.3636233.
Sakurai, 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. 5 (29 aprile 2023): 169. http://dx.doi.org/10.3390/fi15050169.
Toor, 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. 7 (9 aprile 2020): 2131. http://dx.doi.org/10.3390/s20072131.
Kumar, 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 agosto 2021): e660. http://dx.doi.org/10.7717/peerj-cs.660.
Dries, Anton, e Ulrich Rückert. "Adaptive concept drift detection". Statistical Analysis and Data Mining: The ASA Data Science Journal 2, n. 5-6 (18 novembre 2009): 311–27. http://dx.doi.org/10.1002/sam.10054.
Palli, 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. 22 (17 novembre 2022): 11688. http://dx.doi.org/10.3390/app122211688.
Hu, Hanqing, e Mehmed Kantardzic. "Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification". Intelligent Decision Technologies 15, n. 4 (10 gennaio 2022): 609–22. http://dx.doi.org/10.3233/idt-210115.
Sun, 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 giugno 2018): 1–13. http://dx.doi.org/10.1155/2018/4276291.
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.
Wares, Scott, John Isaacs e Eyad Elyan. "Burst Detection-Based Selective Classifier Resetting". Journal of Information & Knowledge Management 20, n. 02 (23 aprile 2021): 2150027. http://dx.doi.org/10.1142/s0219649221500271.
Gâlmeanu, Honorius, e Răzvan Andonie. "Concept Drift Adaptation with Incremental–Decremental SVM". Applied Sciences 11, n. 20 (15 ottobre 2021): 9644. http://dx.doi.org/10.3390/app11209644.
McKay, Helen, Nathan Griffiths, Phillip Taylor, Theo Damoulas e Zhou Xu. "Bi-directional online transfer learning: a framework". Annals of Telecommunications 75, n. 9-10 (ottobre 2020): 523–47. http://dx.doi.org/10.1007/s12243-020-00776-1.
Lu, Ning, Guangquan Zhang e Jie Lu. "Concept drift detection via competence models". Artificial Intelligence 209 (aprile 2014): 11–28. http://dx.doi.org/10.1016/j.artint.2014.01.001.
Mulimani, 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. 4 (1 ottobre 2021): 1–22. http://dx.doi.org/10.4018/ijncr.2021100101.
Kumar, 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. 06 (29 giugno 2021): 49–55. http://dx.doi.org/10.51201/jusst/21/06492.
Sankara 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. 3.6 (4 luglio 2018): 148. http://dx.doi.org/10.14419/ijet.v7i3.6.14959.
Barddal, 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. 1 (gennaio 2015): 26–41. http://dx.doi.org/10.4018/ijncr.2015010102.
Althabiti, Mashail Shaeel, e Manal Abdullah. "CDDM: Concept Drift Detection Model for Data Stream". International Journal of Interactive Mobile Technologies (iJIM) 14, n. 10 (30 giugno 2020): 90. http://dx.doi.org/10.3991/ijim.v14i10.14803.
Sheluhin, Oleg I., Vyacheslav V. Barkov e Airapet G. Simonyan. "Concept drift detection in mobile applications classification using autoencoders". H&ES Research 15, n. 3 (2023): 20–29. http://dx.doi.org/10.36724/2409-5419-2023-15-3-20-29.
Chu, Renjie, Peiyuan Jin, Hanli Qiao e Quanxi Feng. "Intrusion detection in the IoT data streams using concept drift localization". AIMS Mathematics 9, n. 1 (2023): 1535–61. http://dx.doi.org/10.3934/math.2024076.
LEE, Jeonghoon, e Yoon-Joon LEE. "Concept Drift Detection for Evolving Stream Data". IEICE Transactions on Information and Systems E94-D, n. 11 (2011): 2288–92. http://dx.doi.org/10.1587/transinf.e94.d.2288.
Beshah, Yonas Kibret, Surafel Lemma Abebe e Henock Mulugeta Melaku. "Drift Adaptive Online DDoS Attack Detection Framework for IoT System". Electronics 13, n. 6 (7 marzo 2024): 1004. http://dx.doi.org/10.3390/electronics13061004.
Desale, 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. 10 (19 ottobre 2021): 160. http://dx.doi.org/10.3991/ijoe.v17i10.25473.
Mehmood, 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 gennaio 2024): e1827. http://dx.doi.org/10.7717/peerj-cs.1827.
Subha, 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. 5 (31 gennaio 2024): 386–96. http://dx.doi.org/10.17485/ijst/v17i5.1645.
Abdualrhman, 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. 1 (gennaio 2019): 29–48. http://dx.doi.org/10.4018/ijghpc.2019010103.
Adebayo, 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. 7 (30 luglio 2023): 24–48. http://dx.doi.org/10.47760/ijcsmc.2023.v12i07.004.
Manikandaraja, Abishek, Peter Aaby e Nikolaos Pitropakis. "Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection". Computers 12, n. 10 (28 settembre 2023): 195. http://dx.doi.org/10.3390/computers12100195.
Namitha 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. 1 (gennaio 2020): 67–85. http://dx.doi.org/10.4018/ijaeis.2020010104.
Li, 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 marzo 2020): 1–8. http://dx.doi.org/10.1155/2020/4027423.
Omori, 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. 4 (31 luglio 2020): 101–25. http://dx.doi.org/10.5753/isys.2020.832.
Du, L., Q. Song, L. Zhu e X. Zhu. "A Selective Detector Ensemble for Concept Drift Detection". Computer Journal 58, n. 3 (20 giugno 2014): 457–71. http://dx.doi.org/10.1093/comjnl/bxu050.
Zambon, Daniele, Cesare Alippi e Lorenzo Livi. "Concept Drift and Anomaly Detection in Graph Streams". IEEE Transactions on Neural Networks and Learning Systems 29, n. 11 (novembre 2018): 5592–605. http://dx.doi.org/10.1109/tnnls.2018.2804443.
Cabral, Danilo Rafael de Lima, e Roberto Souto Maior de Barros. "Concept drift detection based on Fisher’s Exact test". Information Sciences 442-443 (maggio 2018): 220–34. http://dx.doi.org/10.1016/j.ins.2018.02.054.
Adams, 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. 8 (aprile 2023): 1856–69. http://dx.doi.org/10.14778/3594512.3594517.
Sun, Yingying, Jusheng Mi e Chenxia Jin. "Entropy-based concept drift detection in information systems". Knowledge-Based Systems 290 (aprile 2024): 111596. http://dx.doi.org/10.1016/j.knosys.2024.111596.
Gandhi, Jay, e Vaibhav Gandhi. "Novel Class Detection with Concept Drift in Data Stream - AhtNODE". International Journal of Distributed Systems and Technologies 11, n. 1 (gennaio 2020): 15–26. http://dx.doi.org/10.4018/ijdst.2020010102.
Mahdi, 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. 2 (14 gennaio 2020): 606. http://dx.doi.org/10.3390/app10020606.
Palli, 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. 1 (30 gennaio 2024): 105–39. http://dx.doi.org/10.32890/jict2024.23.1.5.
Sato, 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. 9 (31 dicembre 2022): 1–38. http://dx.doi.org/10.1145/3472752.
Vyawhare, 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. 5 (31 maggio 2022): 47–55. http://dx.doi.org/10.22214/ijraset.2022.42048.
Elkhawaga, 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. 7 (3 luglio 2020): 161. http://dx.doi.org/10.3390/a13070161.
Henke, 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. 4 (28 aprile 2021): 364–86. http://dx.doi.org/10.3897/jucs.66284.
Yang, Rui, Shuliang Xu e Lin Feng. "An Ensemble Extreme Learning Machine for Data Stream Classification". Algorithms 11, n. 7 (17 luglio 2018): 107. http://dx.doi.org/10.3390/a11070107.
Chen, 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. 1 (1 settembre 2021): 012161. http://dx.doi.org/10.1088/1742-6596/2010/1/012161.
Miyata, Yasushi, e Hiroshi Ishikawa. "Concept Drift Detection on Stream Data for Revising DBSCAN". IEEJ Transactions on Electronics, Information and Systems 140, n. 8 (1 agosto 2020): 949–55. http://dx.doi.org/10.1541/ieejeiss.140.949.
Cejnek, Matous, e Ivo Bukovsky. "Concept drift robust adaptive novelty detection for data streams". Neurocomputing 309 (ottobre 2018): 46–53. http://dx.doi.org/10.1016/j.neucom.2018.04.069.
Escovedo, Tatiana, Adriano Koshiyama, Andre Abs da Cruz e Marley Vellasco. "DetectA: abrupt concept drift detection in non-stationary environments". Applied Soft Computing 62 (gennaio 2018): 119–33. http://dx.doi.org/10.1016/j.asoc.2017.10.031.
Zenisek, Jan, Florian Holzinger e Michael Affenzeller. "Machine learning based concept drift detection for predictive maintenance". Computers & Industrial Engineering 137 (novembre 2019): 106031. http://dx.doi.org/10.1016/j.cie.2019.106031.
Yu, 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. 5 (marzo 2019): 3187–215. http://dx.doi.org/10.1016/j.jfranklin.2019.01.043.