Meta-features for meta-learning

Meta-learning is increasingly used to support the recommendation of machine learning algorithms and their configurations. These recommendations are made based on meta-data, consisting of performance evaluations of algorithms and characterizations on prior 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 many 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 an extensive list of meta-features and characterization tools, which can be used as a guide for new practitioners. By identifying particularities and subtle issues related to the characterization measures, this survey points out possible future directions that the development of meta-features for meta-learning can assume. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

Meta-learning is increasingly utilized for recommending machine learning algorithms and configurations, but the inconsistency in describing and computing meta-features hampers reproducibility and comparability of empirical studies. This paper addresses this issue by systematizing and standardizing data characterization measures for classification datasets in meta-learning, while also providing an extensive list of meta-features and characterization tools as a guide for practitioners. The survey highlights the particularities and potential future directions in the development of meta-features for meta-learning.