On the Class Imbalance Problem

By Xinjian Guo, Yilong Yin, Cailing Dong, Gongping Yang, Guang-Tong Zhou. 2008

586 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistical Learning Theory

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Citations in the corpus

WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “Depending on the particular task (see Section 4, for the full list), the class imbalance may be an issue [19], therefore, task-specific splits are also provided.”
A systematic study of the class imbalance problem in convolutional neural networks (2017, background): “Methods for addressing the class imbalance problem can be divided into two main categories [31, 32, 16].”
Feature selection for high-dimensional data (2016, background): “If one appliesmost traditional classifiers on the data set, they are likely to predict everything as the majority class [46].”
Class Imbalance, Redux (2011, background): “The inherently pragmatic nature of the problem has motivated a significant amount of methodological research into learning under imbalance, of which there are at least three surveys [9], [8], [12].”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, background): “The skewed distribution of the training examples makes standard learning classifiers biased, favouring the majority class and cannot detect rare instances [2], [3].”
Selecting Representative Data Sets (2012, methodology): “Some advanced re-sampling methods are based on re-sampling of results of the preliminary classification [44].”
Classification with class imbalance problem: A review (2015, methodology): “Another concern with imbalanced class learning is that most standard classifiers assumed that the domain application datasets are equal in sight [1][20][21][22][23].”