CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data

Class imbalance, where datasets often lack sufficient samples for minority classes, is a persistent challenge in machine learning. Existing solutions often generate synthetic data to mitigate this issue, but they typically struggle with complex data distributions, primarily because they focus on ove...

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Veröffentlicht in:Knowledge and information systems Jg. 67; H. 6; S. 5335 - 5354
Hauptverfasser: Wang, Alex X., Le, Minh Quang, Duong, Huu-Thanh, Van, Bay Nguyen, Nguyen, Binh P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.06.2025
Springer Nature B.V
Schlagworte:
Accuracy
Classification
Computer Science
Data Mining and Knowledge Discovery
Database Management
Datasets
Deep learning
Diffusion models
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Machine learning
Oversampling
Synthetic data
Deep learning
Imbalance data
Data-centric AI
Contrastive learning
Synthetic data
ISSN:0219-1377, 0219-3116
Online-Zugang:Volltext
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Zusammenfassung:Class imbalance, where datasets often lack sufficient samples for minority classes, is a persistent challenge in machine learning. Existing solutions often generate synthetic data to mitigate this issue, but they typically struggle with complex data distributions, primarily because they focus on oversampling the minority class while neglecting the relationships with the majority class. To overcome these limitations, we propose the Contrastive Tabular Variational Autoencoder (CTVAE), which integrates conditional Variational Autoencoders with contrastive learning techniques. CTVAE excels at generating high-quality synthetic samples that capture the intricate data distributions of both minority and majority classes. Additionally, it can be seamlessly integrated with variants of the Synthetic Minority Oversampling Technique (SMOTE) for enhanced effectiveness. Experimental results demonstrate that CTVAE substantially improves classification performance on imbalanced datasets, offering a more robust and holistic solution to the class imbalance problem.
Bibliographie:ObjectType-Article-1
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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-025-02377-7