An evaluation framework for synthetic data generation models
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914109905174528 |
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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 |