Conditional Variational Autoencoder-Based Sampling

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Springer Science and Business Media Deutschland GmbH
Imbalanced 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.
This work is not available in the CUD collection. The version of the scholarly record of this work is published in Lecture Notes in Networks and Systems (2023), available online at:
Autoencoder, Imbalanced data, Sampling
Kamalov, 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, vol 517. Springer, Singapore.