SetConv: A New Approach for Learning from Imbalanced Data

SMOTE: synthetic minority over-sampling technique

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A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches

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Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data

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Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches

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Mwmote-majority weighted minority oversampling technique for imbalanced data set learning

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An instance level analysis of data complexity

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Learning from imbalanced data using ensemble methods and cluster-based undersampling

Parinaz Sobhani, Herna Viktor, and Stan Matwin. 2014 · 2014

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Evolutionary undersampling for imbalanced big data classification

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Resampling-based ensemble methods for online class imbalance learning

Shuo Wang, Leandro L. Minku, and Xin Yao. 2015 · 2015

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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering

Ruining He and Julian J. McAuley. 2016 · 2016

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SemEval-2017 task 4: Sentiment analysis in Twitter

Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017 · 2017

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Semantic abstraction for generalization of tweet classification: An evaluation of incident-related tweets

Axel Schulz, Christian Guckelsberger, and Frederik Janssen. 2017 · 2017

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Multiset feature learning for highly imbalanced data classification

Fei Wu, Xiao-Yuan Jing, Shiguang Shan, Wangmeng Zuo, and Jing-Yu Yang. 2017 · 2017

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An improved fuzzy classifier for imbalanced data

Dandan Yan, Youlong Yang, and Benchong Li. 2017 · 2017

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Deep mlps for imbalanced classification

David Díaz-Vico, Aníbal R. Figueiras-Vidal, and José R. Dorronsoro. 2018 · 2018

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Iterative metric learning for imbalance data classification

Nan Wang, Xibin Zhao, Yu Jiang, and Yue Gao. 2018 · 2018