# Characterizing classification datasets: a study of meta-features for meta-learning

arXiv: https://arxiv.org/abs/1808.10406 · HTML: https://mlsanity.com/paper/1808.10406/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [Learning Abstract Task Representations](https://mlsanity.com/paper/2101.07852/) (2021)
- [Hyperparameter Optimization with Differentiable Metafeatures](https://mlsanity.com/paper/2102.03776/) (2021)
- [MACFE: A Meta-learning and Causality Based Feature Engineering Framework](https://mlsanity.com/paper/2207.04010/) (2022)
- [Toward Efficient Automated Feature Engineering](https://mlsanity.com/paper/2212.13152/) (2022)
- [Evaluating Meta-Feature Selection for the Algorithm Recommendation Problem](https://mlsanity.com/paper/2106.03954/) (2021)
- [Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering](https://mlsanity.com/paper/2602.18348/) (2026)

## Abstract (arXiv)

Meta-learning is increasingly used to support the recommendation of machine learning algorithms and their configurations. Such recommendations are made based on meta-data, consisting of performance evaluations of algorithms on prior datasets, as well as characterizations of these datasets. These characterizations, also called meta-features, describe properties of the data which are predictive for the performance of machine learning algorithms trained on them. Unfortunately, despite being used in a large number of studies, meta-features are not uniformly described, organized and computed, making many empirical studies irreproducible and hard to compare. This paper aims to deal with this by systematizing and standardizing data characterization measures for classification datasets used in meta-learning. Moreover, it presents MFE, a new tool for extracting meta-features from datasets and identifying more subtle reproducibility issues in the literature, proposing guidelines for data characterization that strengthen reproducible empirical research in meta-learning.