Tabular Data Synthesis with GANs for Adaptive AI Models
Pages 242 - 246
Abstract
In situations such as demographics change ML models often perform poorly because the training data does not appropriately represent the environment. Privacy concerns worsen the issue by severely limiting training data. In this paper, we present a framework that utilizes a GAN-based synthesizer to generate synthetic data that not only satisfies user-defined constraints expressed as marginal distributions of selected columns but also strives to preserve the distributions observed in the original data. This framework takes as input an original dataset and a set of user-defined constraints, and synthesizes data that adheres to these constraints while capturing the underlying distributions present in the given data. The result is a customizable and realistic data generation solution that balances constraint satisfaction and preservation of data distributions.We validate and demonstrate the effectiveness of our technique through experimentation.
References
[1]
Mrinal Kanti Baowaly, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen. 2019. Synthesizing electronic health records using improved generative adversarial networks. Journal of the American Medical Informatics Association 26, 3 (2019), 228–241.
[2]
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar. 2007. Privacy, accuracy, and consistency too: a holistic solution to contingency table release. In Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 273–282.
[3]
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002. SMOTE: synthetic minority over-sampling technique. JAIR 16 (2002), 321–357.
[4]
Haipeng Chen, Sushil Jajodia, Jing Liu, Noseong Park, Vadim Sokolov, and VS Subrahmanian. 2019. FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data. In IJCAI. 2074–2080.
[5]
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F Stewart, and Jimeng Sun. 2017. Generating multi-label discrete patient records using generative adversarial networks. In Machine learning for healthcare conference. PMLR, 286–305.
[6]
Graham Cormode, Minos Garofalakis, Peter J Haas, Chris Jermaine, 2011. Synopses for massive data: Samples, histograms, wavelets, sketches. Foundations and Trends® in Databases 4, 1–3 (2011), 1–294.
[7]
Dhimant Ganatara. [n. d.]. Campus Recruitment Analysis. ([n. d.]). https://www.kaggle.com/datasets/benroshan/factors-affecting-campus-placement
[8]
Lovedeep Gondara and Ke Wang. 2018. Mida: Multiple imputation using denoising autoencoders. In Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III 22. Springer, 260–272.
[9]
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS. 2672–2680.
[10]
Diederik P. Kingma and Max Welling. [n. d.]. Auto-Encoding Variational Bayes. In ICLR 2014, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1312.6114
[11]
Ronny Kohavi and Barry Becker. [n. d.]. Census Income Data. ([n. d.]). https://archive.ics.uci.edu/ml/datasets/adult
[12]
Haoran Li, Li Xiong, Lifan Zhang, and Xiaoqian Jiang. 2014. DPSynthesizer: differentially private data synthesizer for privacy preserving data sharing. In Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, Vol. 7. NIH Public Access, 1677.
[13]
Pei-Hsuan Lu, Pang-Chieh Wang, and Chia-Mu Yu. 2019. Empirical evaluation on synthetic data generation with generative adversarial network. In Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics. 1–6.
[14]
Guido Moerkotte, Thomas Neumann, and Gabriele Steidl. 2009. Preventing bad plans by bounding the impact of cardinality estimation errors. Proceedings of the VLDB Endowment 2, 1 (2009), 982–993.
[15]
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim. 2018. Data synthesis based on generative adversarial networks. arXiv preprint arXiv:1806.03384 (2018).
[16]
Yubin Park and Joydeep Ghosh. 2014. PeGS: Perturbed Gibbs Samplers that Generate Privacy-Compliant Synthetic Data.Trans. Data Priv. 7, 3 (2014), 253–282.
[17]
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni. 2016. The synthetic data vault. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 399–410.
[18]
Diptikalyan Saha, Aniya Aggarwal, and Sandeep Hans. 2022. Data synthesis for testing black-box machine learning models. In 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD). 110–114.
[19]
Aivin V Solatorio and Olivier Dupriez. 2023. REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers. arXiv preprint arXiv:2302.02041 (2023).
[20]
Saravanan Thirumuruganathan, Shohedul Hasan, Nick Koudas, and Gautam Das. 2019. Approximate query processing using deep generative models. arXiv preprint arXiv:1903.10000 (2019).
[21]
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019. Modeling tabular data using conditional gan. Advances in neural information processing systems 32 (2019).
[22]
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019. Modeling tabular data using conditional gan. Advances in Neural Information Processing Systems 32 (2019).
[23]
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), 1–41.
Index Terms
- Tabular Data Synthesis with GANs for Adaptive AI Models
Index terms have been assigned to the content through auto-classification.
Recommendations
Controllable Tabular Data Synthesis Using Diffusion Models
SIGMODControllable tabular data synthesis plays a crucial role in numerous applications by allowing users to generate synthetic data with specific conditions. These conditions can include synthesizing tuples with predefined attribute values or creating tuples ...
Improving GAN with inverse cumulative distribution function for tabular data synthesis
AbstractDesigning a generative model to synthesize realistic tabular data is of great significance in data science. Existing tabular data generative models have difficulty in handling complicated and diverse marginal distribution types due to ...
Synthesis of object models from partial models: a CSP perspective
ECAI'02: Proceedings of the 15th European Conference on Artificial IntelligenceIn this work we present an approach for the synthesis of object models (expressed as Constraint Satisfaction Problems, CSPs) from views or partial models (expressed, in their turn, as CSPs as well).
The approach we propose is general enough to consider ...
Comments
Information & Contributors
Information
Published In
January 2024
627 pages
ISBN:9798400716348
DOI:10.1145/3632410
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 January 2024
Check for updates
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
CODS-COMAD 2024
CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)
January 4 - 7, 2024
Bangalore, India
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 113Total Downloads
- Downloads (Last 12 months)73
- Downloads (Last 6 weeks)6
Reflects downloads up to 03 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format