A Preliminary Study of SMOTE on Imbalanced Big Datasets When Dealing with Sparse and Dense High Dimensionality-Reference-Cited by-同舟云学术

A Preliminary Study of SMOTE on Imbalanced Big Datasets When Dealing with Sparse and Dense High Dimensionality

Published:2022 Issue: Volume: Page:46-55
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Bolívar A.^ORCID,García V.^ORCID,Florencia R.^ORCID,Alejo R.^ORCID,Rivera G.^ORCID,Sánchez-Solís J. Patricia^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-07750-0_5

Reference24 articles.

1. Ali, A., Shamsuddin, S.M., Ralescu, A.: Classification with class imbalance problem: a review. Int. J. Adv. Soft Comput. Appl. 7(3), 176–204 (2015)

2. Basgall, M.J., Hasperué, W., Naiouf, M., Fernández, A., Herrera, F.: SMOTE-BD: an exact and scalable oversampling method for imbalanced classification in big data. In: VI Jornadas de Cloud Computing & Big Data (JCC&BD) (La Plata 2018) (2018)

3. Communications in Computer and Information Science;MJ Basgall,2019

4. Blagus, R., Lusa, L.: SMOTE for high-dimensional class-imbalanced data. BMC Bioinform. 14(106), 1–16 (2013)

5. Branco, P., Torgo, L., Ribeiro, R.P.: A survey of predictive modelling under imbalanced distributions. CoRR abs/1505.01658 (2015). http://arxiv.org/abs/1505.01658

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