Classifier Selection for Highly Imbalanced Data Streams with Minority Driven Ensemble-Reference-Cited by-同舟云学术

Classifier Selection for Highly Imbalanced Data Streams with Minority Driven Ensemble

Published:2019 Issue: Volume: Page:626-635
ISSN:0302-9743
Container-title:Artificial Intelligence and Soft Computing
language:
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Author:

Zyblewski Paweł^ORCID,Ksieniewicz Paweł^ORCID,Woźniak Michał^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-20912-4_57

Reference20 articles.

1. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);P Branco,2017

2. Chen, S., He, H.: Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach. Evol. Syst. 2(1), 35–50 (2011)

3. Chen, X.w., Wasikowski, M.: Fast: a ROC-based feature selection metric for small samples and imbalanced data classification problems. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 124–132 (2008)

4. Cruz, R.M.O., Hafemann, L.G., Sabourin, R., Cavalcanti, G.D.C.: DESlib: a dynamic ensemble selection library in Python. arXiv preprint arXiv:1802.04967 (2018)

5. Cruz, R.M., Sabourin, R., Cavalcanti, G.D.: Dynamic classifier selection. Inf. Fusion 41(C), 195–216 (2018)

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