GAN-based approach for data imputation and handling class imbalance using one class ensemble

Class imbalance in real-world datasets is a significant issue that results in bias in the machine learning model and may result in incorrect predictions. In this paper, a GAN-based Multiple Imputation One-Class Ensemble (GMI-OCE) is presented for imbalanced classification in scenarios with missing values in the dataset. The approach uses a hybrid OCC ensemble, incorporating a GAN architecture for imputing missing values and boosting the number of minority class instances without modifying the observed values directly. A two-step bootstrap aggregation is applied using a novel weighting algorithm that considers the accuracy of individual classifiers and their performance on synthetic data. The approach is evaluated on various imbalanced datasets and compared against seven baseline methods. The results indicate that GMI-OCE outperforms in most of the datasets compared to other methods based on various evaluation metrics.