  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.
