EvaGoNet: An integrated network of variational autoencoder and Wasserstein generative adversarial network with gradient penalty for binary classification tasks.

Feature engineering is an effective method for solving classification problems. Many existing feature engineering studies have focused on image or video data and not on structured data. This study proposes EvaGoNet, which refines the decoder module of the Gaussian mixture variational autoencoder using the Wasserstein generative adversarial network with gradient penalty (WGANgp) and embeds the top-ranked original features to update the latent features based on their discriminative powers. Comprehensive experiments show that EvaGoNet-encoded features outperform existing classifiers on 12 benchmark datasets, particularly on the small, imbalanced datasets col (accuracy = 0.8581), spe (accuracy = 1.0000), and leu (accuracy = 0.8021). EvaGoNet-engineered features improve binary classification task outcomes on six high-dimensional, imbalanced bioOMIC datasets. EvaGoNet achieves a medium-ranked training speed among the compared algorithms and considerably fast prediction speeds in the predictions of the testing samples. Therefore, EvaGoNet can be a candidate feature engineering framework for many practical applications that require one training procedure and many prediction tasks of the testing samples. EvaGoNet is implemented in Python TensorFlow and is available at https://healthinformaticslab.org/supp/resources.php.