Iterative minority oversampling and its ensemble for ordinal imbalanced datasets-Reference-Cited by-同舟云学术

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Iterative minority oversampling and its ensemble for ordinal imbalanced datasets

Published:2024-01 Issue: Volume:127 Page:107211
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Wang Ning,Zhang Zhong-Liang,Luo Xing-Gang

Funder

Natural Science Foundation of Zhejiang Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference56 articles.

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

2. A novel approach based on DEMATEL method and patent citation analysis for prioritizing a portfolio of investment projects;Altuntas;Expert Syst. Appl.,2015

3. Evaluation measures for ordinal regression;Baccianella,2009

4. MWMOTE–majority weighted minority oversampling technique for imbalanced data set learning;Barua;IEEE Trans. Knowl. Data Eng.,2012

5. A study of the behavior of several methods for balancing machine learning training data;Batista;ACM SIGKDD Explor. Newsl.,2004

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