# Transforming Datasets to Requested Complexity with Projection-based Many-Objective Genetic Algorithm

arXiv: https://arxiv.org/abs/2507.15132 · HTML: https://mlsanity.com/paper/2507.15132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Sparsity-Probe: Analysis tool for Deep Learning Models](https://mlsanity.com/paper/2105.06849/) (2021)
- [Synthetic Data for Feature Selection](https://mlsanity.com/paper/2211.03035/) (2022)

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

- [LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selection](https://mlsanity.com/paper/2605.09518/) (2026)

## Abstract (arXiv)

The research community continues to seek increasingly more advanced synthetic data generators to reliably evaluate the strengths and limitations of machine learning methods. This work aims to increase the availability of datasets encompassing a diverse range of problem complexities by proposing a genetic algorithm that optimizes a set of problem complexity measures for classification and regression tasks towards specific targets. For classification, a set of 10 complexity measures was used, while for regression tasks, 4 measures demonstrating promising optimization capabilities were selected. Experiments confirmed that the proposed genetic algorithm can generate datasets with varying levels of difficulty by transforming synthetically created datasets to achieve target complexity values through linear feature projections. Evaluations involving state-of-the-art classifiers and regressors revealed a correlation between the complexity of the generated data and the recognition quality.