  Real-world binary classification tasks are in many cases imbalanced, where
the minority class is much smaller than the majority class. This skewness is
challenging for machine learning algorithms as they tend to focus on the
majority and greatly misclassify the minority. Adding synthetic minority
samples to the dataset before training the model is a popular technique to
address this difficulty and is commonly achieved by interpolating minority
samples. Tabular datasets are often multi-modal and contain discrete
(categorical) features in addition to continuous ones which makes interpolation
of samples non-trivial. To address this, we propose a latent space
interpolation framework which (1) maps the multi-modal samples to a dense
continuous latent space using an autoencoder; (2) applies oversampling by
interpolation in the latent space; and (3) maps the synthetic samples back to
the original feature space. We defined metrics to directly evaluate the quality
of the minority data generated and showed that our framework generates better
synthetic data than the existing methods. Furthermore, the superior synthetic
data yields better prediction quality in downstream binary classification
tasks, as was demonstrated in extensive experiments with 27 publicly available
real-world datasets
