Concept drift (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Concept drift" in English language version.

refsWebsite

Global rank English rank

10doi.org

2^nd place

8semanticscholar.org

11^th place

8^th place

2nih.gov

4^th place

1arxiv.org

69^th place

59^th place

1dev.to

low place

1cmswire.com

low place

1informationweek.com

4,522^nd place

2,903^rd place

1books.google.com

3^rd place

1worldcat.org

5^th place

1softxjournal.com

low place

1handle.net

102^nd place

76^th place

arxiv.org

Lu, Jie; Liu, Anjin; Dong, Fan; Gu, Feng; Gama, Joao; Zhang, Guangquan (2018). "Learning under Concept Drift: A Review". IEEE Transactions on Knowledge and Data Engineering: 1. arXiv:2004.05785. doi:10.1109/TKDE.2018.2876857. S2CID 69449458.

books.google.com

Daniel Nichter, Efficient MySQL Performance, 2021, ISBN 1098105060, p. 299

cmswire.com

Girish Pancha, Big Data's Hidden Scourge: Data Drift, CMSWire, April 8, 2016

dev.to

"Driftctl and Terraform, they're two of a kind!"

doi.org

Koggalahewa, Darshika; Xu, Yue; Foo, Ernest (2021). "A Drift Aware Hierarchical Test Based Approach for Combating Social Spammers in Online Social Networks". Data Mining. Communications in Computer and Information Science. Vol. 1504. pp. 47–61. doi:10.1007/978-981-16-8531-6_4. ISBN 978-981-16-8530-9. S2CID 245009299.
Widmer, Gerhard; Kubat, Miroslav (1996). "Learning in the presence of concept drift and hidden contexts". Machine Learning. 23: 69–101. doi:10.1007/BF00116900. S2CID 206767784.
Xia, Yuan; Zhao, Yunlong (2020). "A Drift Detection Method Based on Diversity Measure and McDiarmid's Inequality in Data Streams". Green, Pervasive, and Cloud Computing. Lecture Notes in Computer Science. Vol. 12398. pp. 115–122. doi:10.1007/978-3-030-64243-3_9. ISBN 978-3-030-64242-6. S2CID 227275380.
Lu, Jie; Liu, Anjin; Dong, Fan; Gu, Feng; Gama, Joao; Zhang, Guangquan (2018). "Learning under Concept Drift: A Review". IEEE Transactions on Knowledge and Data Engineering: 1. arXiv:2004.05785. doi:10.1109/TKDE.2018.2876857. S2CID 69449458.
Alippi, C.; Roveri, M. (2007). "Adaptive Classifiers in Stationary Conditions". 2007 International Joint Conference on Neural Networks. IEEE. pp. 1008–13. doi:10.1109/ijcnn.2007.4371096. ISBN 978-1-4244-1380-5. S2CID 16255206.
Gama, J.; Medas, P.; Castillo, G.; Rodrigues, P. (2004). "Learning with Drift Detection". Advances in Artificial Intelligence – SBIA 2004. Springer. pp. 286–295. doi:10.1007/978-3-540-28645-5_29. ISBN 978-3-540-28645-5. S2CID 2606652.
Alippi, C.; Boracchi, G.; Roveri, M. (2011). "A just-in-time adaptive classification system based on the intersection of confidence intervals rule". Neural Networks. 24 (8): 791–800. doi:10.1016/j.neunet.2011.05.012. PMID 21723706.
Widmer, G.; Kubat, M. (1996). "Learning in the presence of concept drift and hidden contexts". Machine Learning. 23 (1): 69–101. doi:10.1007/bf00116900. S2CID 206767784.
Elwell, R.; Polikar, R. (2011). "Incremental Learning of Concept Drift in Nonstationary Environments". IEEE Transactions on Neural Networks. 22 (10): 1517–31. doi:10.1109/tnn.2011.2160459. PMID 21824845. S2CID 9136731.
Céspedes Sisniega, Jaime; López García, Álvaro (2024). "Frouros: An open-source Python library for drift detection in machine learning systems" (PDF). SoftwareX. 26. Elsevier: 101733. doi:10.1016/j.softx.2024.101733. hdl:10261/358367.

handle.net

hdl.handle.net

Céspedes Sisniega, Jaime; López García, Álvaro (2024). "Frouros: An open-source Python library for drift detection in machine learning systems" (PDF). SoftwareX. 26. Elsevier: 101733. doi:10.1016/j.softx.2024.101733. hdl:10261/358367.

informationweek.com

Matthew Magne, "Data Drift Happens: 7 Pesky Problems with People Data", InformationWeek, July 19, 2017

nih.gov

pubmed.ncbi.nlm.nih.gov

Alippi, C.; Boracchi, G.; Roveri, M. (2011). "A just-in-time adaptive classification system based on the intersection of confidence intervals rule". Neural Networks. 24 (8): 791–800. doi:10.1016/j.neunet.2011.05.012. PMID 21723706.
Elwell, R.; Polikar, R. (2011). "Incremental Learning of Concept Drift in Nonstationary Environments". IEEE Transactions on Neural Networks. 22 (10): 1517–31. doi:10.1109/tnn.2011.2160459. PMID 21824845. S2CID 9136731.

semanticscholar.org

api.semanticscholar.org

Koggalahewa, Darshika; Xu, Yue; Foo, Ernest (2021). "A Drift Aware Hierarchical Test Based Approach for Combating Social Spammers in Online Social Networks". Data Mining. Communications in Computer and Information Science. Vol. 1504. pp. 47–61. doi:10.1007/978-981-16-8531-6_4. ISBN 978-981-16-8530-9. S2CID 245009299.
Widmer, Gerhard; Kubat, Miroslav (1996). "Learning in the presence of concept drift and hidden contexts". Machine Learning. 23: 69–101. doi:10.1007/BF00116900. S2CID 206767784.
Xia, Yuan; Zhao, Yunlong (2020). "A Drift Detection Method Based on Diversity Measure and McDiarmid's Inequality in Data Streams". Green, Pervasive, and Cloud Computing. Lecture Notes in Computer Science. Vol. 12398. pp. 115–122. doi:10.1007/978-3-030-64243-3_9. ISBN 978-3-030-64242-6. S2CID 227275380.
Lu, Jie; Liu, Anjin; Dong, Fan; Gu, Feng; Gama, Joao; Zhang, Guangquan (2018). "Learning under Concept Drift: A Review". IEEE Transactions on Knowledge and Data Engineering: 1. arXiv:2004.05785. doi:10.1109/TKDE.2018.2876857. S2CID 69449458.
Alippi, C.; Roveri, M. (2007). "Adaptive Classifiers in Stationary Conditions". 2007 International Joint Conference on Neural Networks. IEEE. pp. 1008–13. doi:10.1109/ijcnn.2007.4371096. ISBN 978-1-4244-1380-5. S2CID 16255206.
Gama, J.; Medas, P.; Castillo, G.; Rodrigues, P. (2004). "Learning with Drift Detection". Advances in Artificial Intelligence – SBIA 2004. Springer. pp. 286–295. doi:10.1007/978-3-540-28645-5_29. ISBN 978-3-540-28645-5. S2CID 2606652.
Widmer, G.; Kubat, M. (1996). "Learning in the presence of concept drift and hidden contexts". Machine Learning. 23 (1): 69–101. doi:10.1007/bf00116900. S2CID 206767784.
Elwell, R.; Polikar, R. (2011). "Incremental Learning of Concept Drift in Nonstationary Environments". IEEE Transactions on Neural Networks. 22 (10): 1517–31. doi:10.1109/tnn.2011.2160459. PMID 21824845. S2CID 9136731.

softxjournal.com

Céspedes Sisniega, Jaime; López García, Álvaro (2024). "Frouros: An open-source Python library for drift detection in machine learning systems" (PDF). SoftwareX. 26. Elsevier: 101733. doi:10.1016/j.softx.2024.101733. hdl:10261/358367.

worldcat.org

Basseville, Michele (1993). Detection of abrupt changes: theory and application. Prentice Hall. ISBN 0-13-126780-9. OCLC 876004326.