Synthetic Data Generation Task

A Synthetic Data Generation Task is a generation task that requires the production of synthetic data records.

• AKA: Synthetic Random Data Generation.
• Context:
  • It can be solved by a Data Generation System (that implements a data generation algorithm).
  • It can require the simulation of a Data Generation Process.
  • It can range from being a Numerical Data Generation Task to being a Categorical Data Generation Task to being a Hybrid Data Generation Task.
  • It can address issues such as data privacy and data scarcity by providing alternative datasets for analysis and model training.
  • It can be used in various domains including computer vision, natural language processing, healthcare, and business.
  • ...
• Example(s):
  • a Random Number Generation Task.
  • creating a 5-dimensional Identity Matrix Data Structure.
  • Patient Record Generation for a medical study.
  • …
• Counter-Example(s):
  • a Data Processing Task.
• See: Data Masking, Simulation.

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References

2023

• (Lu, Shen et al., 2023) ⇒ Yingzhou Lu, Minjie Shen, Huazheng Wang, Xiao Wang, Capucine van Rechem, and Wenqi Wei. (2023). “Machine Learning for Synthetic Data Generation: A Review.” In: arXiv preprint arXiv:2302.04062. doi:10.48550/arXiv.2302.04062
  • NOTE:
    • The paper highlights that synthetic data generation addresses significant challenges in machine learning, such as data quality, scarcity, and privacy issues, by providing alternative datasets that mimic real-world data.
    • The paper describes various application domains where synthetic data generation is impactful, including computer vision, natural language processing, speech, healthcare, and business.
    • The paper emphasizes the importance of addressing privacy and fairness concerns in synthetic data generation, noting that sensitive information can be inferred from synthesized data.
    • The paper discusses the impact of synthetic data on regulatory compliance, particularly in fields like healthcare, where sharing real patient data is restricted due to privacy regulations.

2009

• (Gentle, 2009) ⇒ James E. Gentle. (2009). “Computational Statistics." Springer. ISBN:978-0-387-98143-7
  • QUOTE: Many exercises require the student to generate artificial data. While such datasets may lack any apparent intrinsic interest, I believe that they are often the best for learning how a statistical method works. One of my firm beliefs is
    If I understand something, I can simulate it.

1999

• (Melli, 1999) => Gabor Melli. (1999). “The datgen Dataset Generator." Version 3.1 http://www.datasetgenerator.com

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