A supervised generative optimization approach for tabular data

Synthetic data generation has emerged as a crucial topic for financial institutions, driven by multiple factors, such as privacy protection and data augmentation. Many algorithms have been proposed for synthetic data generation but reaching the consensus on which method we should use for the specifi...

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Bibliographic Details
Published inarXiv.org
Main Authors Nakamura-Sakai, Shinpei, Hamad, Fadi, Obitayo, Saheed, Potluru, Vamsi K
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 09.05.2024
Subjects
Algorithms
Data augmentation
Optimization
Synthetic data
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Summary:Synthetic data generation has emerged as a crucial topic for financial institutions, driven by multiple factors, such as privacy protection and data augmentation. Many algorithms have been proposed for synthetic data generation but reaching the consensus on which method we should use for the specific data sets and use cases remains challenging. Moreover, the majority of existing approaches are ``unsupervised'' in the sense that they do not take into account the downstream task. To address these issues, this work presents a novel synthetic data generation framework. The framework integrates a supervised component tailored to the specific downstream task and employs a meta-learning approach to learn the optimal mixture distribution of existing synthetic distributions.
ISSN:2331-8422