# Synthetic Data for Feature Selection

arXiv: https://arxiv.org/abs/2211.03035 · HTML: https://mlsanity.com/paper/2211.03035/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 6 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://mlsanity.com/paper/1802.07814/) (2018)
- [Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods](https://mlsanity.com/paper/2012.04580/) (2020)
- [Ship classification from overhead imagery using synthetic data and domain adaptation](https://mlsanity.com/paper/1905.03894/) (2019)

## Built on by (most cited first, 2 of 2)

- [Transforming Datasets to Requested Complexity with Projection-based Many-Objective Genetic Algorithm](https://mlsanity.com/paper/2507.15132/) (2025)
- [Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection](https://mlsanity.com/paper/2605.21561/) (2026)

## Abstract (arXiv)

Feature selection is an important and active field of research in machine learning and data science. Our goal in this paper is to propose a collection of synthetic datasets that can be used as a common reference point for feature selection algorithms. Synthetic datasets allow for precise evaluation of selected features and control of the data parameters for comprehensive assessment. The proposed datasets are based on applications from electronics in order to mimic real life scenarios. To illustrate the utility of the proposed data we employ one of the datasets to test several popular feature selection algorithms. The datasets are made publicly available on GitHub and can be used by researchers to evaluate feature selection algorithms.