Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring
Y Yuan, J Wei, H Huang, W Jiao, J Wang… - … Applications of Artificial …, 2023 - Elsevier
In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …
Effective class-imbalance learning based on SMOTE and convolutional neural networks
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …
Evolving ensembles using multi-objective genetic programming for imbalanced classification
Abstract Multi-objective Genetic Programming (MGP) plays a prominent role in generating
Pareto optimal classifier sets and making trade-offs among multiple classes adaptively …
Pareto optimal classifier sets and making trade-offs among multiple classes adaptively …
Density weighted twin support vector machines for binary class imbalance learning
BB Hazarika, D Gupta - Neural Processing Letters, 2022 - Springer
Usually the real-world (RW) datasets are imbalanced in nature, ie, there is a significant
difference between the number of negative and positive class samples in the datasets …
difference between the number of negative and positive class samples in the datasets …
Fundus image-label pairs synthesis and retinopathy screening via GANs with class-imbalanced semi-supervised learning
Retinopathy is the primary cause of irreversible yet preventable blindness. Numerous deep-
learning algorithms have been developed for automatic retinal fundus image analysis …
learning algorithms have been developed for automatic retinal fundus image analysis …
Representation of imprecision in deep neural networks for image classification
Quantification and reduction of uncertainty in deep-learning techniques have received much
attention but ignored how to characterize the imprecision caused by such uncertainty. In …
attention but ignored how to characterize the imprecision caused by such uncertainty. In …
Anomaly detection in additive manufacturing processes using supervised classification with imbalanced sensor data based on generative adversarial network
Supervised classification methods have been widely utilized for the quality assurance of the
advanced manufacturing process, such as additive manufacturing (AM) for anomaly …
advanced manufacturing process, such as additive manufacturing (AM) for anomaly …
Neural Networks Learn Specified Information for Imbalanced Data Classification
Z ao Huang, Y Sang, Y Sun, J Lv - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Imbalanced data problem is a classic topic in artificial intelligence. Neural network
approaches to solve this problem mostly rely on resampling or reweighting strategies …
approaches to solve this problem mostly rely on resampling or reweighting strategies …
[HTML][HTML] Imbalanced spectral data analysis using data augmentation based on the generative adversarial network
Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific
widths in the frequency domain. These features act as unique identities for material …
widths in the frequency domain. These features act as unique identities for material …
A distributed approach to meteorological predictions: addressing data imbalance in precipitation prediction models through federated learning and GANs
E Jafarigol, TB Trafalis - Computational Management Science, 2024 - Springer
The classification of weather data involves categorizing meteorological phenomena into
classes, thereby facilitating nuanced analyses and precise predictions for various sectors …
classes, thereby facilitating nuanced analyses and precise predictions for various sectors …