Ben Hassine, M., & Mili, L. (2025). Empirical copula-based data augmentation for mixed-type datasets: a robust approach for synthetic data generation. Peer J Computer Science, 1-31. https://doi.org/10.7717/peerj-cs.3228
ISO-690 (author-date, English)BEN HASSINE, Mohsen und MILI, Lamine, 2025. Empirical copula-based data augmentation for mixed-type datasets: a robust approach for synthetic data generation. Peer J Computer Science. 1 Oktober 2025. P. 1-31. DOI 10.7717/peerj-cs.3228.
Modern Language Association 9th editionBen Hassine, M., und L. Mili. „Empirical Copula-Based Data Augmentation for Mixed-Type Datasets: A Robust Approach for Synthetic Data Generation.“. Peer J Computer Science, Oktober 2025, S. 1-31, https://doi.org/10.7717/peerj-cs.3228.
Mohr Siebeck - Recht (Deutsch - Österreich)Ben Hassine, Mohsen/Mili, Lamine: Empirical copula-based data augmentation for mixed-type datasets: a robust approach for synthetic data generation., Peer J Computer Science 2025, 1-31.
Emerald - HarvardBen Hassine, M. und Mili, L. (2025), „Empirical copula-based data augmentation for mixed-type datasets: a robust approach for synthetic data generation.“, Peer J Computer Science, S. 1-31.