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Imputation of missing data with class imbalance using conditional generative adversarial networks

Awan, S.E., Bennamoun, M., Sohel, F., Sanfilippo, F. and Dwivedi, G. (2021) Imputation of missing data with class imbalance using conditional generative adversarial networks. Neurocomputing, 453 . pp. 164-171.

Link to Published Version: https://doi.org/10.1016/j.neucom.2021.04.010
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Abstract

Missing data is a common problem faced with real-world datasets. Imputation is a widely used technique to estimate the missing data. State-of-the-art imputation approaches model the distribution of observed data to approximate the missing values. Such an approach usually models a single distribution for the entire dataset, which overlooks the class-specific characteristics of the data. Class-specific characteristics are especially useful when there is a class imbalance. We propose a new method for imputing missing data based on its class-specific characteristics by adapting the popular Conditional Generative Adversarial Networks (CGAN). Our Conditional Generative Adversarial Imputation Network (CGAIN) imputes the missing data using class-specific distributions, which can produce the best estimates for the missing values. We tested our approach on baseline datasets and achieved superior performance compared with the state-of-the-art and popular imputation approaches.

Item Type: Journal Article
Murdoch Affiliation(s): Information Technology, Mathematics and Statistics
Publisher: Elsevier BV
Copyright: © 2021 Elsevier B.V.
URI: http://researchrepository.murdoch.edu.au/id/eprint/61041
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