Oversampling and undersampling in data analysis (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Oversampling and undersampling in data analysis" in English language version.

refsWebsite

Global rank English rank

15doi.org

2^nd place

9semanticscholar.org

11^th place

8^th place

7worldcat.org

5^th place

2github.com

383^rd place

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2jair.org

low place

2arxiv.org

69^th place

59^th place

2handle.net

102^nd place

76^th place

2springer.com

274^th place

309^th place

2nih.gov

4^th place

1oup.com

485^th place

440^th place

1ugr.es

4,970^th place

low place

1acm.org

1,185^th place

840^th place

1wiley.com

222^nd place

297^th place

1harvard.edu

18^th place

17^th place

1books.google.com

3^rd place

1youtube.com

9^th place

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acm.org (Global: 1,185^th place; English: 840^th place)

dl.acm.org

Batista, Gustavo E. A. P. A.; Prati, Ronaldo C.; Monard, Maria Carolina (2004-06-01). "A study of the behavior of several methods for balancing machine learning training data". SIGKDD Explor. Newsl. 6 (1): 20–29. doi:10.1145/1007730.1007735. ISSN 1931-0145.

arxiv.org (Global: 69^th place; English: 59^th place)

Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. (2002-06-01). "SMOTE: Synthetic Minority Over-sampling Technique". Journal of Artificial Intelligence Research. 16: 321–357. arXiv:1106.1813. doi:10.1613/jair.953. ISSN 1076-9757. S2CID 1554582.
Elor, Yotam; Averbuch-Elor, Hadar (2022). "To SMOTE, or not to SMOTE?". arXiv:2201.08528v3 [cs.LG].

books.google.com (Global: 3^rd place; English: 3^rd place)

Encyclopedia of Machine Learning. (2011). Deutschland: Springer. Page 193, https://books.google.com/books?id=i8hQhp1a62UC&pg=PT193

doi.org (Global: 2^nd place; English: 2^nd place)

Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. (2002-06-01). "SMOTE: Synthetic Minority Over-sampling Technique". Journal of Artificial Intelligence Research. 16: 321–357. arXiv:1106.1813. doi:10.1613/jair.953. ISSN 1076-9757. S2CID 1554582.
van den Goorbergh, Ruben; van Smeden, Maarten; Timmerman, Dirk; Van Calster, Ben (2022-09-01). "The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression". Journal of the American Medical Informatics Association. 29 (9): 1525–1534. doi:10.1093/jamia/ocac093. ISSN 1527-974X. PMC 9382395. PMID 35686364.
Chawla, Nitesh V.; Herrera, Francisco; Garcia, Salvador; Fernandez, Alberto (2018-04-20). "SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary". Journal of Artificial Intelligence Research. 61: 863–905. doi:10.1613/jair.1.11192. hdl:10481/56411. ISSN 1076-9757.
He, Haibo; Bai, Yang; Garcia, Edwardo A.; Li, Shutao (June 2008). "ADASYN: Adaptive synthetic sampling approach for imbalanced learning" (PDF). 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). pp. 1322–1328. doi:10.1109/IJCNN.2008.4633969. ISBN 978-1-4244-1820-6. S2CID 1438164. Retrieved 5 December 2022.
Shorten, Connor; Khoshgoftaar, Taghi M. (2019). "A survey on Image Data Augmentation for Deep Learning". Mathematics and Computers in Simulation. 6 60. springer. doi:10.1186/s40537-019-0197-0.
Dal Pozzolo, Andrea; Caelen, Olivier; Bontempi, Gianluca (2015-01-01). "When is Undersampling Effective in Unbalanced Classification Tasks?". Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Springer. doi:10.1007/978-3-319-23528-8_13.
Batista, Gustavo E. A. P. A.; Prati, Ronaldo C.; Monard, Maria Carolina (2004-06-01). "A study of the behavior of several methods for balancing machine learning training data". SIGKDD Explor. Newsl. 6 (1): 20–29. doi:10.1145/1007730.1007735. ISSN 1931-0145.
Zhu, Mingzhu; Xu, Chao; Wu, Yi-Fang Brook (2013-07-22). IFME: information filtering by multiple examples with under-sampling in a digital library environment. ACM. pp. 107–110. doi:10.1145/2467696.2467736. ISBN 9781450320771. S2CID 13279787.
Ribeiro, Rita P.; Moniz, Nuno (2020-09-01). "Imbalanced regression and extreme value prediction". Machine Learning. 109 (9): 1803–1835. doi:10.1007/s10994-020-05900-9. ISSN 1573-0565. S2CID 222143074.
Torgo, Luís; Branco, Paula; Ribeiro, Rita P.; Pfahringer, Bernhard (June 2015). "Resampling strategies for regression". Expert Systems. 32 (3): 465–476. doi:10.1111/exsy.12081. S2CID 205129966.
Torgo, Luís; Ribeiro, Rita P.; Pfahringer, Bernhard; Branco, Paula (2013). "SMOTE for Regression". In Correia, Luís; Reis, Luís Paulo; Cascalho, José (eds.). Progress in Artificial Intelligence. Lecture Notes in Computer Science. Vol. 8154. Berlin, Heidelberg: Springer. pp. 378–389. doi:10.1007/978-3-642-40669-0_33. hdl:10289/8518. ISBN 978-3-642-40669-0. S2CID 16253787.
Moniz, Nuno; Branco, Paula; Torgo, Luís (2017-05-01). "Resampling strategies for imbalanced time series forecasting". International Journal of Data Science and Analytics. 3 (3): 161–181. doi:10.1007/s41060-017-0044-3. ISSN 2364-4168. S2CID 25975914.
Oliveira, Mariana; Moniz, Nuno; Torgo, Luís; Santos Costa, Vítor (2021-09-01). "Biased resampling strategies for imbalanced spatio-temporal forecasting". International Journal of Data Science and Analytics. 12 (3): 205–228. doi:10.1007/s41060-021-00256-2. ISSN 2364-4168. S2CID 210931099.
Haibo He; Garcia, E.A. (2009). "Learning from Imbalanced Data". IEEE Transactions on Knowledge and Data Engineering. 21 (9): 1263–1284. Bibcode:2009ITKDE..21.1263H. doi:10.1109/TKDE.2008.239. S2CID 206742563.
"Imbalance correction led to models with strong miscalibration without better ability to distinguish between patients with and without the outcome event. The inaccurate probability estimates reduce the clinical utility of the model, because decisions about treatment are ill-informed.", The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression, 2022, Ruben van den Goorbergh, Maarten van Smeden, Dirk Timmerman, Ben Van Calster https://doi.org/10.1093/jamia/ocac093

github.com (Global: 383^rd place; English: 320^th place)

"Scikit-learn-contrib/Imbalanced-learn". GitHub. 25 October 2021.
"Analyticalmindsltd/Smote_variants". GitHub. 26 October 2021.

handle.net (Global: 102^nd place; English: 76^th place)

hdl.handle.net

Chawla, Nitesh V.; Herrera, Francisco; Garcia, Salvador; Fernandez, Alberto (2018-04-20). "SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary". Journal of Artificial Intelligence Research. 61: 863–905. doi:10.1613/jair.1.11192. hdl:10481/56411. ISSN 1076-9757.
Torgo, Luís; Ribeiro, Rita P.; Pfahringer, Bernhard; Branco, Paula (2013). "SMOTE for Regression". In Correia, Luís; Reis, Luís Paulo; Cascalho, José (eds.). Progress in Artificial Intelligence. Lecture Notes in Computer Science. Vol. 8154. Berlin, Heidelberg: Springer. pp. 378–389. doi:10.1007/978-3-642-40669-0_33. hdl:10289/8518. ISBN 978-3-642-40669-0. S2CID 16253787.

harvard.edu (Global: 18^th place; English: 17^th place)

ui.adsabs.harvard.edu

Haibo He; Garcia, E.A. (2009). "Learning from Imbalanced Data". IEEE Transactions on Knowledge and Data Engineering. 21 (9): 1263–1284. Bibcode:2009ITKDE..21.1263H. doi:10.1109/TKDE.2008.239. S2CID 206742563.

jair.org (Global: low place; English: low place)

Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. (2002-06-01). "SMOTE: Synthetic Minority Over-sampling Technique". Journal of Artificial Intelligence Research. 16: 321–357. arXiv:1106.1813. doi:10.1613/jair.953. ISSN 1076-9757. S2CID 1554582.
Chawla, Nitesh V.; Herrera, Francisco; Garcia, Salvador; Fernandez, Alberto (2018-04-20). "SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary". Journal of Artificial Intelligence Research. 61: 863–905. doi:10.1613/jair.1.11192. hdl:10481/56411. ISSN 1076-9757.

nih.gov (Global: 4^th place; English: 4^th place)

ncbi.nlm.nih.gov

van den Goorbergh, Ruben; van Smeden, Maarten; Timmerman, Dirk; Van Calster, Ben (2022-09-01). "The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression". Journal of the American Medical Informatics Association. 29 (9): 1525–1534. doi:10.1093/jamia/ocac093. ISSN 1527-974X. PMC 9382395. PMID 35686364.

pubmed.ncbi.nlm.nih.gov

van den Goorbergh, Ruben; van Smeden, Maarten; Timmerman, Dirk; Van Calster, Ben (2022-09-01). "The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression". Journal of the American Medical Informatics Association. 29 (9): 1525–1534. doi:10.1093/jamia/ocac093. ISSN 1527-974X. PMC 9382395. PMID 35686364.

oup.com (Global: 485^th place; English: 440^th place)

academic.oup.com

van den Goorbergh, Ruben; van Smeden, Maarten; Timmerman, Dirk; Van Calster, Ben (2022-09-01). "The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression". Journal of the American Medical Informatics Association. 29 (9): 1525–1534. doi:10.1093/jamia/ocac093. ISSN 1527-974X. PMC 9382395. PMID 35686364.

semanticscholar.org (Global: 11^th place; English: 8^th place)

api.semanticscholar.org

Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. (2002-06-01). "SMOTE: Synthetic Minority Over-sampling Technique". Journal of Artificial Intelligence Research. 16: 321–357. arXiv:1106.1813. doi:10.1613/jair.953. ISSN 1076-9757. S2CID 1554582.
He, Haibo; Bai, Yang; Garcia, Edwardo A.; Li, Shutao (June 2008). "ADASYN: Adaptive synthetic sampling approach for imbalanced learning" (PDF). 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). pp. 1322–1328. doi:10.1109/IJCNN.2008.4633969. ISBN 978-1-4244-1820-6. S2CID 1438164. Retrieved 5 December 2022.
Zhu, Mingzhu; Xu, Chao; Wu, Yi-Fang Brook (2013-07-22). IFME: information filtering by multiple examples with under-sampling in a digital library environment. ACM. pp. 107–110. doi:10.1145/2467696.2467736. ISBN 9781450320771. S2CID 13279787.
Ribeiro, Rita P.; Moniz, Nuno (2020-09-01). "Imbalanced regression and extreme value prediction". Machine Learning. 109 (9): 1803–1835. doi:10.1007/s10994-020-05900-9. ISSN 1573-0565. S2CID 222143074.
Torgo, Luís; Branco, Paula; Ribeiro, Rita P.; Pfahringer, Bernhard (June 2015). "Resampling strategies for regression". Expert Systems. 32 (3): 465–476. doi:10.1111/exsy.12081. S2CID 205129966.
Torgo, Luís; Ribeiro, Rita P.; Pfahringer, Bernhard; Branco, Paula (2013). "SMOTE for Regression". In Correia, Luís; Reis, Luís Paulo; Cascalho, José (eds.). Progress in Artificial Intelligence. Lecture Notes in Computer Science. Vol. 8154. Berlin, Heidelberg: Springer. pp. 378–389. doi:10.1007/978-3-642-40669-0_33. hdl:10289/8518. ISBN 978-3-642-40669-0. S2CID 16253787.
Moniz, Nuno; Branco, Paula; Torgo, Luís (2017-05-01). "Resampling strategies for imbalanced time series forecasting". International Journal of Data Science and Analytics. 3 (3): 161–181. doi:10.1007/s41060-017-0044-3. ISSN 2364-4168. S2CID 25975914.
Oliveira, Mariana; Moniz, Nuno; Torgo, Luís; Santos Costa, Vítor (2021-09-01). "Biased resampling strategies for imbalanced spatio-temporal forecasting". International Journal of Data Science and Analytics. 12 (3): 205–228. doi:10.1007/s41060-021-00256-2. ISSN 2364-4168. S2CID 210931099.
Haibo He; Garcia, E.A. (2009). "Learning from Imbalanced Data". IEEE Transactions on Knowledge and Data Engineering. 21 (9): 1263–1284. Bibcode:2009ITKDE..21.1263H. doi:10.1109/TKDE.2008.239. S2CID 206742563.

springer.com (Global: 274^th place; English: 309^th place)

link.springer.com

Dal Pozzolo, Andrea; Caelen, Olivier; Bontempi, Gianluca (2015-01-01). "When is Undersampling Effective in Unbalanced Classification Tasks?". Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Springer. doi:10.1007/978-3-319-23528-8_13.
Torgo, Luís; Ribeiro, Rita P.; Pfahringer, Bernhard; Branco, Paula (2013). "SMOTE for Regression". In Correia, Luís; Reis, Luís Paulo; Cascalho, José (eds.). Progress in Artificial Intelligence. Lecture Notes in Computer Science. Vol. 8154. Berlin, Heidelberg: Springer. pp. 378–389. doi:10.1007/978-3-642-40669-0_33. hdl:10289/8518. ISBN 978-3-642-40669-0. S2CID 16253787.

ugr.es (Global: 4,970^th place; English: low place)

sci2s.ugr.es

He, Haibo; Bai, Yang; Garcia, Edwardo A.; Li, Shutao (June 2008). "ADASYN: Adaptive synthetic sampling approach for imbalanced learning" (PDF). 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). pp. 1322–1328. doi:10.1109/IJCNN.2008.4633969. ISBN 978-1-4244-1820-6. S2CID 1438164. Retrieved 5 December 2022.

wiley.com (Global: 222^nd place; English: 297^th place)

onlinelibrary.wiley.com

Torgo, Luís; Branco, Paula; Ribeiro, Rita P.; Pfahringer, Bernhard (June 2015). "Resampling strategies for regression". Expert Systems. 32 (3): 465–476. doi:10.1111/exsy.12081. S2CID 205129966.

worldcat.org (Global: 5^th place; English: 5^th place)

search.worldcat.org

Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. (2002-06-01). "SMOTE: Synthetic Minority Over-sampling Technique". Journal of Artificial Intelligence Research. 16: 321–357. arXiv:1106.1813. doi:10.1613/jair.953. ISSN 1076-9757. S2CID 1554582.
van den Goorbergh, Ruben; van Smeden, Maarten; Timmerman, Dirk; Van Calster, Ben (2022-09-01). "The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression". Journal of the American Medical Informatics Association. 29 (9): 1525–1534. doi:10.1093/jamia/ocac093. ISSN 1527-974X. PMC 9382395. PMID 35686364.
Chawla, Nitesh V.; Herrera, Francisco; Garcia, Salvador; Fernandez, Alberto (2018-04-20). "SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary". Journal of Artificial Intelligence Research. 61: 863–905. doi:10.1613/jair.1.11192. hdl:10481/56411. ISSN 1076-9757.
Batista, Gustavo E. A. P. A.; Prati, Ronaldo C.; Monard, Maria Carolina (2004-06-01). "A study of the behavior of several methods for balancing machine learning training data". SIGKDD Explor. Newsl. 6 (1): 20–29. doi:10.1145/1007730.1007735. ISSN 1931-0145.
Ribeiro, Rita P.; Moniz, Nuno (2020-09-01). "Imbalanced regression and extreme value prediction". Machine Learning. 109 (9): 1803–1835. doi:10.1007/s10994-020-05900-9. ISSN 1573-0565. S2CID 222143074.
Moniz, Nuno; Branco, Paula; Torgo, Luís (2017-05-01). "Resampling strategies for imbalanced time series forecasting". International Journal of Data Science and Analytics. 3 (3): 161–181. doi:10.1007/s41060-017-0044-3. ISSN 2364-4168. S2CID 25975914.
Oliveira, Mariana; Moniz, Nuno; Torgo, Luís; Santos Costa, Vítor (2021-09-01). "Biased resampling strategies for imbalanced spatio-temporal forecasting". International Journal of Data Science and Analytics. 12 (3): 205–228. doi:10.1007/s41060-021-00256-2. ISSN 2364-4168. S2CID 210931099.

youtube.com (Global: 9^th place; English: 13^th place)

Guillaume Lemaitre EuroSciPy 2023 - Get the best from your scikit-learn classifier https://www.youtube.com/watch?v=6YnhoCfArQo

Oversampling and undersampling in data analysis (English Wikipedia)

acm.org (Global: 1,185th place; English: 840th place)

dl.acm.org

arxiv.org (Global: 69th place; English: 59th place)

books.google.com (Global: 3rd place; English: 3rd place)

doi.org (Global: 2nd place; English: 2nd place)

github.com (Global: 383rd place; English: 320th place)

handle.net (Global: 102nd place; English: 76th place)