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Imputation of missing data with class imbalance using conditional generative adversarial networks
Journal article   Peer reviewed

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

S.E. Awan, M. Bennamoun, F. Sohel, F. Sanfilippo and G. Dwivedi
Neurocomputing, Vol.453, pp.164-171
2021
DOI: 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.

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80 Record Views
63 Times Cited - Web of Science

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Collaboration types
Domestic collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.61 Artificial Intelligence & Machine Learning
4.61.145 Classification Algorithms
Web Of Science research areas
Computer Science, Artificial Intelligence
ESI research areas
Computer Science
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