High-Fidelity Synthetic Data Applications for Data Augmentation

Author:

Wang Zhenchen,Draghi Barbara,Rotalinti Ylenia,Lunn Darren,Myles Puja

Abstract

The use of high-fidelity synthetic data for data augmentation is an area of growing interest in data science. In this chapter, the concept of synthetic data is introduced, and different types of synthetic data are discussed in terms of their utility or fidelity. Approaches to synthetic data generation are presented and compared with computer modelling and simulation approaches, highlighting the unique benefits of high-fidelity synthetic data. One of the main applications of high-fidelity synthetic data is supporting the training and validation of machine learning algorithms, where it can provide a virtually unlimited amount of diverse and high-quality data to improve the accuracy and robustness of models. Furthermore, high-fidelity synthetic data can address missing data and biases due to under-sampling using techniques such as BayesBoost, as well as boost sample sizes in scenarios where the real data is based on a small sample. Another important application is generating virtual patient cohorts, such as digital twins, to estimate counterfactuals in silico trials, allowing for better prediction of treatment outcomes and personalised medicine. The chapter concludes by identifying areas for further research in the field, including developing more efficient and accurate synthetic data generation methods and exploring the ethical implications of using synthetic data.

Publisher

IntechOpen

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3