Improving SMOTE via fusing conditional VAE for data-adaptive noise filtering Improving SMOTE via fusing conditional VAE for data-adaptive noise filtering

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 improvement in class imbalance data compared to the conventional model, Synthetic Minority Oversam...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 55; no. 12; p. 841
Main Authors: Hong, Sungchul, An, Seunghwan, Jeon, Jong-June
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2025
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Classification
Computer Science
Data augmentation
Data points
Datasets
Deep learning
Machine learning
Machines
Manufacturing
Mechanical Engineering
Methods
Neural networks
Processes
Variational autoencoder
SMOTE
Imbalanced classification
Oversampling
Noise filtering
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary: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 improvement in 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 s). 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.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-025-06692-y