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

Fengfeng Zhou

Abstract:

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 highdimensional,
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.