# Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates

arXiv: https://arxiv.org/abs/2409.02079 · HTML: https://mlsanity.com/paper/2409.02079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [Synthetic Data for Deep Learning](https://mlsanity.com/paper/1909.11512/) (2019)
- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression](https://mlsanity.com/paper/2202.09101/) (2022)
- [Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates](https://mlsanity.com/paper/2107.04974/) (2021)
- [Interpretable Machine Learning for Self-Service High-Risk Decision-Making](https://mlsanity.com/paper/2205.04032/) (2022)

## Built on by (most cited first, 1 of 1)

- [High-Dimensional Data Classification in Concentric Coordinates](https://mlsanity.com/paper/2507.18450/) (2025)

## 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.