CARBO: Clustering and rotation based oversampling for class imbalance learning-Reference-Cited by-同舟云学术

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CARBO: Clustering and rotation based oversampling for class imbalance learning

Published:2024-09 Issue: Volume:300 Page:112196
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Paul Mahit Kumar^ORCID,Pal Biprodip^ORCID,Sattar A.H.M. Sarowar,Siddique A.S.M. Mustakim Rahman^ORCID,Hasan Md. Al Mehedi

Publisher

Elsevier BV

Reference52 articles.

1. Efficient data perturbation for privacy preserving and accurate data stream mining;Chamikara;Pervasive Mob. Comput.,2018

2. Under-sampling class imbalanced datasets by combining clustering analysis and instance selection;Tsai;Inform. Sci.,2019

3. SMOTEBoost: Improving prediction of the minority class in boosting;Chawla,2003

4. To combat multi-class imbalanced problems by means of over-sampling techniques;Abdi;IEEE Trans. Knowl. Data Eng.,2015

5. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches;Fernández;Knowl.-Based Syst.,2013

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