Kamalov, FiruzAli-Gombe, AdamuMoussa, Sherif2023-02-152023-02-15© 20232023Kamalov, F., Ali-Gombe, A., Moussa, S. (2023). Conditional Variational Autoencoder-Based Sampling. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, 517, pp. 661 - 669. Springer, Singapore. https://doi.org/10.1007/978-981-19-5224-1_66978-981195223-423673370https://hdl.handle.net/20.500.12519/743Imbalanced data distribution implies an uneven distribution of class labels in data which can lead to classification bias in machine learning models. The present paper proposes an autoencoder-based sampling approach to balance the data. Concretely, the proposed method utilizes a conditional variational autoencoder (VAE) to learn the latent variables underpinning the distribution of minority labels. Then, the trained encoder is employed to produce new minority samples to equalize the sample distribution. The results of numerical experiments reveal the potency of the suggested technique on several datasets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en-USLicense to reuse abstract has been secured from Springer Nature and Copyright Clearance Center.AutoencoderImbalanced dataSamplingConference PaperCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.