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Survey on deep learning with class imbalance [PDF]
Journal of Big Data, 2019 The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real ...
Justin M. Johnson, Taghi M. Khoshgoftaar
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A Survey on Class Imbalance in Federated Learning [PDF]
CoRR, 2023 Federated learning, which allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data, is an emerging distributed learning technique due to its nature of privacy preservation. However, it has been found that models trained with federated learning usually have worse performance than ...
Jing Zhang +3 more
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The class imbalance problem in deep learning
Machine Learning, 2022 Deep learning has recently unleashed the ability for Machine learning (ML) to make unparalleled strides. It did so by confronting and successfully addressing, at least to a certain extent, the knowledge bottleneck that paralyzed ML and artificial intelligence for decades.
Kushankur Ghosh +5 more
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Low-shot learning and class imbalance: a survey
Journal of Big DataThe tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot learning” (LSL)—at first glance are quite similar to the long-standing task of class imbalanced learning; specifically, they aim to learn classes for which there is little ...
Preston Billion Polak +2 more
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Navigating extreme class imbalance in suicide risk prediction [PDF]
Frontiers in PsychiatryBackgroundThe implementation of suicide risk models is challenging because the conditions in which they are developed often do not reflect those in which they are being used.
Christopher Kitchen +7 more
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Optimizing Class Imbalance in Facial Expression Recognition Using Dynamic Intra-Class Clustering [PDF]
BiomimeticsWhile deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction ...
Qingdu Li +8 more
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A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer. [PDF]
Diagnostics (Basel), 2022 Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer ...
Walsh R, Tardy M.
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Effects of Class Imbalance Countermeasures on Interpretability
IEEE AccessThe widespread use of artificial intelligence (AI) in more and more real-world applications is accompanied by challenges that are not obvious at first glance. In machine learning, class imbalance, characterized by an imbalance in the frequency of classes,
David Cemernek +2 more
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A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data [PDF]
BMC Medical Informatics and Decision MakingClass imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers.
Samuel Cusworth +2 more
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Federated Learning with Class Imbalance Reduction [PDF]
2021 29th European Signal Processing Conference (EUSIPCO), 2021 Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to privacy concerns, the raw data on devices could not be available for centralized server. Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged ...
Miao Yang +4 more
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