Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering

TL;DR

This paper enhances the SMOTE data augmentation technique for imbalanced datasets by integrating Variational Autoencoders to better select and augment minority class data, leading to improved classification performance.

Contribution

The paper introduces a novel framework that fuses VAE with SMOTE, systematically filtering and augmenting data based on density and class difficulty in a latent space.

Findings

Improved classification accuracy on imbalanced datasets.

Enhanced data augmentation effectiveness over traditional SMOTE.

Better minority class representation with VAE-guided sampling.

Abstract

Recent advances in a generative neural network model extend the development of data augmentation methods. However, the augmentation methods based on the modern generative models fail to achieve notable performance for class imbalance data compared to the conventional model, Synthetic Minority Oversampling Technique (SMOTE). We investigate the problem of the generative model for imbalanced classification and introduce a framework to enhance the SMOTE algorithm using Variational Autoencoders (VAE). Our approach systematically quantifies the density of data points in a low-dimensional latent space using the VAE, simultaneously incorporating information on class labels and classification difficulty. Then, the data points potentially degrading the augmentation are systematically excluded, and the neighboring observations are directly augmented on the data space. Empirical studies on several imbalanced datasets represent that this simple process innovatively improves the conventional SMOTE algorithm over the deep learning models. Consequently, we conclude that the selection of minority data and the interpolation in the data space are beneficial for imbalanced classification problems with a relatively small number of data points.