Synthesising Multi-Modal Minority Samples for Tabular Data
This work addresses data imbalance issues in tabular classification, particularly for domains with mixed data types, offering an incremental improvement over existing oversampling techniques.
The paper tackles the challenge of imbalanced binary classification in tabular data with multi-modal and discrete features by proposing a latent space interpolation framework for generating synthetic minority samples, which produces better synthetic data than existing methods and improves prediction quality across 27 real-world datasets.
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