ND-S: an oversampling algorithm based on natural neighbor and density peaks clustering-Reference-Cited by-同舟云学术

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ND-S: an oversampling algorithm based on natural neighbor and density peaks clustering

Published:2022-12-23 Issue:8 Volume:79 Page:8668-8698
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Guo Ming,Lu Jia

Funder

the scientific and technological innovation project of double-city economic circle construction in Chengdu-Chongqing area

Chongqing University Innovation Research Group funding

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-022-04965-8.pdf

Reference43 articles.

1. Tarekegn AN, Giacobini M, Michalak K (2021) A review of methods for imbalanced multi-label classification. Pattern Recognit 118:107965

2. Xu Z, Shen D, Nie T, Kou Y, Yin N, Han X (2021) A cluster-based oversampling algorithm combining smote and k-means for imbalanced medical data. Inform Sci 572:574–589

3. Liu Q, Wang D, Jia Y, Luo S, Wang C (2022) A multi-task based deep learning approach for intrusion detection. Knowl-Based Syst 238:107852

4. Sun J, Lang J, Fujita H, Li H (2018) Imbalanced enterprise credit evaluation with dte-sbd: decision tree ensemble based on smote and bagging with differentiated sampling rates. Inform Sci 425:76–91

5. Chakraborty T, Chakraborty AK (2020) Hellinger net: a hybrid imbalance learning model to improve software defect prediction. IEEE Transact Reliab 70(2):481–494

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