Abstract
Imbalanced datasets often result in poor predictive model performance. To address this, minority class sample expansion is used, but two challenges remain. The first is to use algorithms to learn the main features of minority class samples, and the second is to differentiate the generated data from the majority class samples. To tackle these challenges in binary classification, we propose the Discriminant-Autoencoder (D-AE) algorithm. It has two mechanisms based on our insights. Firstly, an autoencoder is used to learn the main features of minority class samples by reconstructing the data with added noise. Secondly, a discriminator is trained on the raw data to distinguish the generated data from the majority class samples. Our proposed loss function, Discriminant-\(L_\theta \), balances the discriminant and reconstruction losses. Results from experiments on three datasets show that D-AE outperforms baseline algorithms and improves dataset applicability.
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Wang, G. et al. (2024). D-AE: A Discriminant Encode-Decode Nets for Data Generation. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_6
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