Learning from Imbalanced Data

By Haibo He, Edwardo A. Garcia. 2009

4,409 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Representation & Self-Supervised Learning

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Citations in the corpus

LVIS: A Dataset for Large Vocabulary Instance Segmentation (2019, background): “Resampling training data is a common strategy for training models on class imbalanced datasets [32, 11, 25, 27].”
Imbalanced Continual Learning with Partitioning Reservoir Sampling (2020, background): “The long-tailed data distribution is both an enduring and pervasive problem in machine learning [40,36], as most real-world datasets are inherently imbalanced [70,63,98,80,65].”
Large-Scale Long-Tailed Recognition in an Open World (2019, background): “We refer the readers to [17] for a detailed review.”
A systematic study of the class imbalance problem in convolutional neural networks (2017, background): “Methods for addressing class imbalance can be divided into two main categories [29].”
The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling (2020, background): “Both techniques and their extensions are surveyed in [9].”
Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach (2016, methodology): “To overcome these possible sources of inaccuracy when evaluating the GH-VFDT, we make use of the G-mean metric (He & Garcia 2009).”
A Survey of Network-based Intrusion Detection Data Sets (2019, background): “AZSecure contains for example the CTU-13 data set by Garcia et al. [3] or the Unified Host and Network Data Set by Turcotte et al. [76].”
A comprehensive data level analysis for cancer diagnosis on imbalanced data (2019, methodology): “For medical industry, such error is more costly than classifying a noncancerous patient as cancerous [19].”
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data (2019, background): “All eight LCLU classes had the same number of training and evaluation samples so that the OA is not biased towards classes with more training samples (He and Garcia 2008).”
Data Augmentation for Deep-Learning-Based Electroencephalography. (2020, background): “It thus worth noting that methods have been developed to deal with such imbalanced data [12].”