Survey of resampling techniques for improving classification performance in unbalanced datasets

By Ajinkya More. 2016

241 citations recorded by the source corpus.

A number of classification problems need to deal with data imbalance between classes. Often it is desired to have a high recall on the minority class while maintaining a high precision on the majority class. In this paper, we review a number of resampling techniques proposed in literature to handle unbalanced datasets and study their effect on classification performance.

Subfields: Artificial Intelligence

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Citations in the corpus

Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure (2019, methodology): “Resampling for class imbalance:Resampling approaches have largely focused on addressing class imbalances [26, 34], as opposed to biases within individual classes.”
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification (2021, methodology): “Typical methods include data re-sampling [1, 8, 35, 4, 28], loss re-weighting [26, 7, 30, 33], margin modification [3], and data augmentation [19, 6, 29].”
Language-Grounded Indoor 3D Semantic Segmentation in the Wild (2022, background): “…towards data-sampling-based re-balancing, for instance random oversampling of underrepresented classes [3,54,50], sampling novel poses of known categories [32], undersampling overrepresented classes [35], frequency-based sampling [25], as well as feature-based or generative sampling [40,39,55].”
Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2018, methodology): “In order to have a more balanced dataset for training, we performed data augmentation, mainly through oversampling by re-sampling and duplicating [37] some data from the Aff-”
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks (2021, methodology): “Data-level approaches seek to make the class distribution more balanced, using over-sampling or down-sampling techniques[8, 26]; algorithm-level approaches typically introduce different mis-classification penalties or prior probabilities for different classes [11, 22, 44]; and hybrid approaches [9,…”
BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition (2019, unknown): “, resampling [25, 1, 14, 1, 11, 2, 7, 21, 4] and cost-sensitive re-weighting [13, 28, 5, 23].”