Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates
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Abstract (arXiv)
Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This paper proposes a unified approach to both synthetic data generation (SDG) and automated data labeling (ADL) with a unified SDG-ADL algorithm. SDG-ADL uses multidimensional (n-D) representations of data visualized losslessly with General Line Coordinates (GLCs), relying on reversible GLC properties to visualize n-D data in multiple GLCs. This paper demonstrates use of the new Circular Coordinates in Static and Dynamic forms, used with Parallel Coordinates and Shifted Paired Coordinates, since each GLC exemplifies unique data properties, such as interattribute n-D distributions and outlier detection. The approach is interactively implemented in computer software with the Dynamic Coordinates Visualization system (DCVis). Results with real data are demonstrated in case studies, evaluating impact on classifiers.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 3 Sep 2024.
Builds on
7 of 15 references.
References · 7
- 2019Modeling Tabular data using Conditional GANcited by 695
- 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 193
- 2019Synthetic Data for Deep Learningcited by 113
- 2018Synthesizing Tabular Data using Generative Adversarial Networkscited by 92
- 2022The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regressioncited by 27
- 2021Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinatescited by 4
- 2022Interpretable Machine Learning for Self-Service High-Risk Decision-Makingcited by 2
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