Results 11 to 20 of about 154,181 (262)
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 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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Experimental Comparison of Classification Methods under Class Imbalance [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2021 The class imbalance problem is prevalent in many domains including medical, natural language processing, image recognition, economic and geographic areas etc.
Hui Chen, Mengru Ji
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Class Imbalance Learning [PDF]
Advanced Computing and Communications, 2017 Data classification task assigns labels to data points using a model that is learned from a collection of pre-labeled data points. The Class Imbalance Learning (CIL) problem is concerned with the performance of classification algorithms in the presence of under-represented data and severe class distribution skews.
Sudarsun Santhiappan +1 more
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Addressing Class Imbalance in Federated Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution and quantity of the training data on the clients' side may lead to significant challenges such as class imbalance and non-IID (non-independent and identically distributed) data,
Lixu Wang +3 more
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Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data
Journal of Big Data, 2019 The integrity of modern network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are consistently shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation ...
Chad L. Calvert, Taghi M. Khoshgoftaar
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Multi-fairness Under Class-Imbalance
, 2022 Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced. The class-imbalance problem is more severe for the often underrepresented protected group (e.g. female, non-white, etc.) in the critical minority class.
Arjun Roy 0001 +2 more
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A Survey on Class Imbalance in Federated Learning
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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Measuring the class-imbalance extent of multi-class problems [PDF]
Pattern Recognition Letters, 2017 TIN2013-41272P, IT609-13, AP2008 ...
Jonathan Ortigosa-Hernández +2 more
openaire +2 more sources