Results 21 to 30 of about 154,181 (262)
Class Balanced Loss for Image Classification
IEEE Access, 2020 In the study of image classification, neural network learning relies heavily on datasets. Due to variability in the difficulty of collecting images in reality, datasets tend to have class imbalance problems, which undoubtedly increases the difficulty of ...
Lin Wang +4 more
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Stop Oversampling for Class Imbalance Learning: A Review
IEEE Access, 2022 For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is,
Ahmad S. Tarawneh +3 more
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Progressive Boosting for Class Imbalance
CoRR, 2017 Pattern recognition applications often suffer from skewed data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification systems designed using skewed data tend to recognize the majority class better than the minority class of interest.
Roghayeh Soleymani +2 more
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Addressing class imbalance in soil movement predictions [PDF]
Natural Hazards and Earth System SciencesLandslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat.
P. Kumar +3 more
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Cost-Sensitive Pattern-Based classification for Class Imbalance problems
IEEE Access, 2019 In several problems, contrast pattern-based classifiers produce high accuracy and provide an explanation of the result in terms of the patterns used for classification. However, class imbalance problems are a great challenge for these classifiers because
Octavio Loyola-Gonzalez +3 more
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Tackling class imbalance with ranking
2016 International Joint Conference on Neural Networks (IJCNN), 2016 In classification, when there is a disproportion in the number of observations in each class, the data is said to be class imbalance. Class imbalance is pervasive in real world applications of data classification and has been the focus of much research.
Ricardo P. M. Cruz +3 more
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Energies, 2022 Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class ...
Kui Wang, Jie Wan, Gang Li, Hao Sun
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Class imbalance techniques for high energy physics
SciPost Physics, 2019 A common problem in a high energy physics experiment is extracting a signal from a much larger background. Posed as a classification task, there is said to be an imbalance in the number of samples belonging to the signal class versus the number of ...
Christopher W. Murphy
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Breast Cancer Diagnosis from Perspective of Class Imbalance [PDF]
Iranian Journal of Medical Physics, 2019 Introduction: Breast cancer is the second cause of mortality among women. Early detection is the only rescue to reduce the risk of breast cancer mortality.
Jue Zhang, Li Chen
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A Class Imbalance Loss for Imbalanced Object Recognition
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 The class imbalance problem exists widely in vision data. In these imbalanced datasets, the majority classes dominate the loss and influence the gradient.
Linbin Zhang +5 more
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