An Approach for Mining Imbalanced Datasets Combining Specialized Oversampling and Undersampling Methods [PDF]
, 2023 Joanna Jędrzejowicz +1 more
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Predicting individual perceptual scent impression from imbalanced dataset using mass spectrum of odorant molecules. [PDF]
Sci Rep, 2022 Debnath T, Nakamoto T.
europepmc +1 more source
Machine Learning Model for Imbalanced Cholera Dataset in Tanzania
The Scientific World Journal, 2019 Cholera epidemic remains a public threat throughout history, affecting vulnerable population living with unreliable water and substandard sanitary conditions.
Judith Leo +2 more
doaj +1 more source
Apple Leaf Disease Identification with a Small and Imbalanced Dataset Based on Lightweight Convolutional Networks. [PDF]
Sensors (Basel), 2021 Li L, Zhang S, Wang B.
europepmc +1 more source
Traffic Sign Recognition System for Imbalanced Dataset [PDF]
Journal of Computers, 2017 TÜMÜKLÜ ÖZYER, Gülşah +2 more
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RegCGAN: Resampling with Regularized CGAN for Imbalanced Big Data Problem
AxiomsWe consider the imbalanced data problem involving a new class of resampling-based models for classification. These models are variants of the conditional generative adversarial networks.
Liwen Xu, Ximeng Wang
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Improving the Performance of Sentiment Classification on Imbalanced Datasets With Transfer Learning [PDF]
, 2019 Zheng Xiao, L. Wang, Jiayi Du
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Algorithm Selection for Deep Active Learning with Imbalanced Datasets [PDF]
, 2023 Jifan Zhang +3 more
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Fault diagnosis methods for imbalanced samples of hydraulic pumps based on DA-DCGAN
Scientific ReportsStatus monitoring and fault diagnosis of mechanical equipment are vital for ensuring operational safety. However, real-world diagnostic scenarios often suffer from limited and imbalanced fault data, affecting model accuracy and reliability.
Yang Zhao +4 more
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Hybrid classification approach for imbalanced datasets
, 2018 The research area of imbalanced dataset has been attracted increasing attention from both academic and industrial areas, because it poses a serious issues for so many supervised learning problems. Since the number of majority class dominates the number of minority class are from minority class, if training dataset includes all data in order to fit a ...
openaire +3 more sources