An evaluation framework for synthetic data generation models

Fuente: arXiv
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Auteurs principaux: Livieris, Ioannis E., Alimpertis, Nikos, Domalis, George, Tsakalidis, Dimitris
Format: Preprint
Publié: 2024
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author Livieris, Ioannis E.
Alimpertis, Nikos
Domalis, George
Tsakalidis, Dimitris
author_facet Livieris, Ioannis E.
Alimpertis, Nikos
Domalis, George
Tsakalidis, Dimitris
contents Nowadays, the use of synthetic data has gained popularity as a cost-efficient strategy for enhancing data augmentation for improving machine learning models performance as well as addressing concerns related to sensitive data privacy. Therefore, the necessity of ensuring quality of generated synthetic data, in terms of accurate representation of real data, consists of primary importance. In this work, we present a new framework for evaluating synthetic data generation models' ability for developing high-quality synthetic data. The proposed approach is able to provide strong statistical and theoretical information about the evaluation framework and the compared models' ranking. Two use case scenarios demonstrate the applicability of the proposed framework for evaluating the ability of synthetic data generation models to generated high quality data. The implementation code can be found in https://github.com/novelcore/synthetic_data_evaluation_framework.
format Preprint
id arxiv_https___arxiv_org_abs_2404_08866
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle An evaluation framework for synthetic data generation models
Livieris, Ioannis E.
Alimpertis, Nikos
Domalis, George
Tsakalidis, Dimitris
Machine Learning
Artificial Intelligence
Nowadays, the use of synthetic data has gained popularity as a cost-efficient strategy for enhancing data augmentation for improving machine learning models performance as well as addressing concerns related to sensitive data privacy. Therefore, the necessity of ensuring quality of generated synthetic data, in terms of accurate representation of real data, consists of primary importance. In this work, we present a new framework for evaluating synthetic data generation models' ability for developing high-quality synthetic data. The proposed approach is able to provide strong statistical and theoretical information about the evaluation framework and the compared models' ranking. Two use case scenarios demonstrate the applicability of the proposed framework for evaluating the ability of synthetic data generation models to generated high quality data. The implementation code can be found in https://github.com/novelcore/synthetic_data_evaluation_framework.
title An evaluation framework for synthetic data generation models
topic Machine Learning
Artificial Intelligence
url https://arxiv.org/abs/2404.08866