KSMOTEEN: A Cluster Based Hybrid Sampling Model for Imbalance Class Data
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4071-4_51
Reference32 articles.
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3. Mathew J et al (2017) Classification of imbalanced data by oversampling in kernel space of support vector machines. IEEE Trans Neural Netw Learn Syst 29(9):4065–4076
4. Bader-El-Den M, Teitei E, Perry T (2018) Biased random forest for dealing with the class imbalance problem. IEEE Trans Neural Netw Learn Syst 30(7):2163–2172
5. Beyan C, Fisher R (2015) Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recogn 48(5):1653–1672
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