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We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models\u2019 performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.<\/jats:p>","DOI":"10.1145\/3657294","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T12:26:23Z","timestamp":1712751983000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["GANs in the Panorama of Synthetic Data Generation Methods"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5801-2739","authenticated-orcid":false,"given":"Bruno","family":"Vaz","sequence":"first","affiliation":[{"name":"Faculty of Sciences, University of Porto, Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0507-7504","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Figueira","sequence":"additional","affiliation":[{"name":"Computer Science, University of Porto, Porto, Portugal and INESC TEC, Porto, Portugal"}]}],"member":"320","published-online":{"date-parts":[[2024,12,14]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","first-page":"59","article-title":"Synthetic establishment microdata around the world","volume":"28","author":"Abowd J. 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TabFairGAN: Fair tabular data generation with generative adversarial networks. https:\/\/arxiv.org\/abs\/2109.00666"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/ICSITech.2017.8257170","volume-title":"2017 3rd International Conference on Science in Information Technology (ICSITech)","author":"Aditsania Annisa","year":"2017","unstructured":"Annisa Aditsania, Aldo Lionel Saonard et al. 2017. Handling imbalanced data in churn prediction using adasyn and backpropagation algorithm. In 2017 3rd International Conference on Science in Information Technology (ICSITech) 533\u2013536."},{"key":"e_1_3_2_10_2","first-page":"2642","volume-title":"International Conference on Machine Learning, (PMLR\u201917)","author":"Odena Augustus","year":"2017","unstructured":"Augustus Odena, Christopher Olah, and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier GANs. In International Conference on Machine Learning, (PMLR\u201917), 2642\u20132651."},{"key":"e_1_3_2_11_2","unstructured":"Bachl Maximilian and Daniel C. Ferreira. 2019. City-GAN: Learning architectural styles using a custom conditional GAN architecture. https:\/\/arxiv.org\/abs\/1907.05280"},{"issue":"6","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s00162-021-00593-9","article-title":"Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks","volume":"35","author":"Siddani Bhargav","year":"2021","unstructured":"Bhargav Siddani, S. Balachandar, William C. Moore, Yunchao Yang, and Ruogu Fang. 2021. Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks. 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Springer, 316\u2013326."},{"key":"e_1_3_2_15_2","first-page":"447","volume-title":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","author":"Lu Chao","year":"2020","unstructured":"Chao Lu, Shaofu Lin, Xiliang Liu, and Hui Shi. 2020. Telecom fraud identification based on ADASYN and random forest. In 2020 5th International Conference on Computer and Communication Systems (ICCCS). IEEE, 447\u2013452."},{"key":"e_1_3_2_16_2","first-page":"785","volume-title":"The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Tianqi Chen","year":"2016","unstructured":"Chen Tianqi and Carlos Guestrin. 2016. XGBoost: A scalable tree boosting system. In The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785\u2013794."},{"key":"e_1_3_2_17_2","first-page":"1","article-title":"C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling","volume":"11","author":"Drummond Chris","year":"2003","unstructured":"Chris Drummond, Robert C. Holte. 2003. C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In Workshop on Learning from Imbalanced Datasets II, Vol. 11. Citeseer. 1\u20138.","journal-title":"Workshop on Learning from Imbalanced Datasets II"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/978-3-642-01307-2_43","volume-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Bunkhumpornpat Chumphol","year":"2009","unstructured":"Chumphol Bunkhumpornpat, Krung Sinapiromsaran, and Chidchanok Lursinsap. 2009. Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 475\u2013482."},{"issue":"3","key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TIT.1968.1054142","article-title":"Approximating discrete probability distributions with dependence trees","volume":"14","author":"Chow C. K.","year":"1968","unstructured":"C. K. Chow and C. N. Liu. 1968. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory 14, 3 (1968), 462\u2013467.","journal-title":"IEEE Transactions on Information Theory"},{"key":"e_1_3_2_20_2","first-page":"29","volume-title":"Artificial Intelligence Research and Development: Proceedings of the 15th International Conference of the Catalan Association for Artificial Intelligence","volume":"248","author":"Riafio D.","year":"2012","unstructured":"D. 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Advances in Neural Information Processing Systems 27 (2014), 1--9.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_33_2","first-page":"502","article-title":"Chapter 14: Autoencoders","author":"Goodfellow Ian","year":"2016","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Chapter 14: Autoencoders. Deep Learning. MIT Press (2016), 502\u2013525.","journal-title":"Deep Learning"},{"key":"e_1_3_2_34_2","first-page":"281","volume-title":"5th Berkeley Symposium on Mathematical Statistiscs and Probability","author":"MacQueen J.","year":"1967","unstructured":"J. MacQueen. 1967. Classification and analysis of multivariate observations. In 5th Berkeley Symposium on Mathematical Statistiscs and Probability, 281\u2013297."},{"key":"e_1_3_2_35_2","volume-title":"The IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","author":"Chen Jianhui","year":"2019","unstructured":"Jianhui Chen and James J. Little. 2019. Sports camera calibration via synthetic data. In The IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops."},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.inffus.2019.07.006","article-title":"Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with smote and time weighting","volume":"54","author":"Sun Jie","year":"2020","unstructured":"Jie Sun, Hui Li, Hamido Fujita, Binbin Fu, and Wenguo Ai. 2020. Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with smote and time weighting. Information Fusion 54 (2020), 128\u2013144.","journal-title":"Information Fusion"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ins.2017.10.017","article-title":"Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates","volume":"425","author":"Sun Jie","year":"2018","unstructured":"Jie Sun, Jie Lang, Hamido Fujita, and Hui Li. 2018. Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. 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Gang of GANs: Generative adversarial networks with maximum margin ranking. https:\/\/arxiv.org\/abs\/1704.04865"},{"issue":"4","key":"e_1_3_2_40_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3134428","article-title":"PrivBayes: Private data release via Bayesian networks","volume":"42","author":"Zhang Jun","year":"2017","unstructured":"Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2017. PrivBayes: Private data release via Bayesian networks. ACM Transactions on Database Systems (TODS) 42, 4 (2017), 1\u201341.","journal-title":"ACM Transactions on Database Systems (TODS)"},{"issue":"1","key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: A data mining perspective","volume":"19","author":"Shu Kai","year":"2017","unstructured":"Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter 19, 1 (2017), 22\u201336.","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"e_1_3_2_42_2","first-page":"1","volume-title":"Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data","author":"Emam Khaled","year":"2020","unstructured":"Khaled Emam, Lucy Mosquera, and Richard Hoptroff. 2020. Chapter 1: Introducing synthetic data generation. Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data. O'Reilly Media, Inc., 1\u201322."},{"key":"e_1_3_2_43_2","first-page":"69","volume-title":"Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data","author":"Emam Khaled","year":"2020","unstructured":"Khaled Emam, Lucy Mosquera, and Richard Hoptroff. 2020. Chapter 4: Evaluating synthetic data utility. Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data. O'Reilly Media, Inc., 69\u201394."},{"key":"e_1_3_2_44_2","first-page":"213","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV)","author":"Shmelkov Konstantin","year":"2018","unstructured":"Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari. 2018. How good is my GAN? In Proceedings of the European Conference on Computer Vision (ECCV), 213\u2013229."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2020.00164"},{"key":"e_1_3_2_46_2","unstructured":"Lei Xu and Kalyan Veeramachaneni. 2018. Synthesizing tabular data using generative adversarial networks. https:\/\/arxiv.org\/abs\/1811.11264"},{"key":"e_1_3_2_47_2","unstructured":"Lei Xu Maria Skoularidou Alfredo Cuesta-Infante and Kalyan Veeramachaneni. 2019. Modeling tabular data using conditional GAN. https:\/\/arxiv.org\/abs\/1907.00503"},{"key":"e_1_3_2_48_2","volume-title":"UCI Machine Learning Repository","author":"Lichman M.","year":"2013","unstructured":"M. Lichman. 2013. UCI Machine Learning Repository, http:\/\/archive.ics.uci.edu\/ml. University of California, School of Information and Computer Science, Irvine, CA."},{"key":"e_1_3_2_49_2","first-page":"89","volume-title":"2012 11th International Conference on Machine Learning and Applications","volume":"2","year":"2012","unstructured":"Lara Lusa. 2012. Evaluation of SMOTE for high-dimensional class-imbalanced microarray data. In 2012 11th International Conference on Machine Learning and Applications, Vol. 2. IEEE, 89\u201394."},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1145\/3395352.3402622","article-title":"Data augmentation with conditional GAN for automatic modulation classification","author":"Patel Mansi","year":"2020","unstructured":"Mansi Patel, Xuyu Wang, and Shiwen Mao. 2020. Data augmentation with conditional GAN for automatic modulation classification. In The 2nd ACM Workshop on Wireless Security and Machine Learning, 31\u201336.","journal-title":"The 2nd ACM Workshop on Wireless Security and Machine Learning"},{"key":"e_1_3_2_51_2","volume-title":"31st Conference on Neural Information Processing Systems (NIPS\u201917)","author":"Heusel Martin","year":"2018","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2018. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In 31st Conference on Neural Information Processing Systems (NIPS\u201917)."},{"key":"e_1_3_2_52_2","volume-title":"The 6th International Conference on Computing Methodologies and Communication (ICCMC\u201922)","author":"Mohana Pradhyumna P.","year":"2022","unstructured":"Pradhyumna P. Mohana. 2022. A survey of modern deep learning based generative adversarial networks (GANs). In The 6th International Conference on Computing Methodologies and Communication (ICCMC\u201922)."},{"issue":"22","key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"220505","DOI":"10.1103\/PhysRevLett.128.220505","article-title":"Entangling quantum generative adversarial networks","volume":"128","author":"Niu Murphy Yuezhen","year":"2022","unstructured":"Murphy Yuezhen Niu, Alexander Zlokapa, Michael Broughton, Sergio Boixo, Masoud Mohseni, Vadim Smelyanskyi, and Hartmut Neven. 2022. Entangling quantum generative adversarial networks. Physical Review Letters 128, 22 (2022), 220505.","journal-title":"Physical Review Letters"},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla Nitesh V.","year":"2002","unstructured":"Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16 (2022), 321\u2013357.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"e_1_3_2_55_2","unstructured":"Noseong Park Mahmoud Mohammadi Kshitij Gorde Sushil Jajodia Hongkyu Park and Youngmin Kim. 2018. Data synthesis based on generative adversarial networks. https:\/\/arxiv.org\/abs\/1806.03384"},{"key":"e_1_3_2_56_2","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent Pascal","year":"2010","unstructured":"Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research 11 (2010), 3371\u20133408.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_57_2","unstructured":"L. Perez and J. Wang. 2017. The effectiveness of data augmentation in image classification using deep learning. https:\/\/arxiv.org\/abs\/1712.04621"},{"key":"e_1_3_2_58_2","first-page":"3567","article-title":"Data programming: Creating large training sets, quickly","volume":"30","author":"Ratner A.","year":"2017","unstructured":"A. Ratner, C. De Sa, S. Wu, D. Selsam, and C. R\u00e9. 2017. Data programming: Creating large training sets, quickly. Advances in Neural Information Processing Systems 30 (2017), 3567\u20133575.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_59_2","first-page":"234","volume-title":"MICCAI 2015","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In MICCAI 2015, Lecture Notes in Computer Science, Vol. 9351. 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MetroGAN: Simulating urban morphology with generative adversarial network. https:\/\/arxiv.org\/abs\/2207.02590","DOI":"10.1145\/3534678.3539239"},{"key":"e_1_3_2_78_2","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume":"80","author":"Xu W.","year":"2019","unstructured":"W. Xu, G. J. Qi, and B. Li. 2019. Representation learning on graphs with jumping knowledge networks. In The 35th International Conference on Machine Learning 80 (2019), 5453\u20135462.","journal-title":"The 35th International Conference on Machine Learning"},{"key":"e_1_3_2_79_2","doi-asserted-by":"crossref","unstructured":"Yasir Alanazi Nobuo Sato Pawel Ambrozewicz Astrid N. Hiller Blin Wally Melnitchouk Marco Battaglieri Tianbo Liu and Yaohang Li. 2021. 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