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arXiv · 2012.00220

Imputation of Missing Data with Class Imbalance using Conditional Generative Adversarial Networks

Saqib Ejaz Awan·Mohammed Bennamoun·Ferdous Sohel·Frank M Sanfilippo·Girish Dwivedi
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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, such as Generative Adversarial Imputation Nets (GAIN), 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 benchmark datasets and achieved superior performance compared with the state-of-the-art and popular imputation approaches.

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Saqib Ejaz Awan, Mohammed Bennamoun, Ferdous Sohel, Frank M Sanfilippo, Girish Dwivedi. 2020-12-01. Imputation of Missing Data with Class Imbalance using Conditional Generative Adversarial Networks. https://arxiv.org/abs/2012.00220

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