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

TL;DR

This paper standardizes and systematizes meta-features for classification datasets in meta-learning, introduces a new extraction tool, and promotes reproducibility in empirical research.

Contribution

It provides a standardized framework for dataset meta-features, introduces MFE tool, and offers guidelines to improve reproducibility in meta-learning studies.

Findings

Developed a comprehensive set of standardized meta-features

Created MFE, a tool for extracting meta-features from datasets

Identified reproducibility issues in existing meta-learning research

Abstract

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.