The new approach first uses GAN-based oversampling to generate the initial balanced dataset and then applies a novel adaptive neighborhood-based weighted ...
Jul 25, 2022 · The new approach first uses GAN-based oversampling to generate the initial balanced dataset and then applies a novel adaptive neighborhood-based ...
In particular, generative adversarial network (GAN)-based oversampling can capture the true data distribution of minority class samples and generate new samples ...
Sep 2, 2024 · In this research, we suggested a hybrid method based on tabular GANs, called CTGAN-ENN, to address class overlap and imbalanced data in datasets of customers ...
May 29, 2024 · In this work, we thus propose a novel GAN-based hybrid sampling method. The new approach first uses GAN-based oversampling to generate the ...
A GAN-based hybrid sampling method for imbalanced customer classification · List of references · Publications that cite this publication.
Jan 11, 2022 · In this paper, we propose a hybrid GAN approach to solve the data imbalance problem to enhance recommendation systems' performance.
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CTGAN-ENN: A tabular GAN-based Hybrid Sampling Method for Imbalanced and Overlapped Data in Customer Churn Prediction ; Best F1-Score (0.994) in Mobile dataset ...
Sep 28, 2021 · We introduce Generative Adversarial Networks (GAN), a deep learning model with strong performance on general data, to time series classification.
In this research, we suggested a hybrid method based on tabular GANs, called CTGAN-ENN, to address class overlap and imbalanced data in datasets of customers ...