An Analysis of Public Datasets for Hierarchical Classification - Maia, Gustavo Vieira; Coelho, Frederico Gualberto Ferreira (Anais do Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware); 2025: Anais do XXII Congresso Latino-Americano de Software Livre e Tecnologias AbertasDO - 10.5753/latinoware.2025.16302, 2025)
Hierarchical classification is a machine learning task that leverages inherent parent-child relationships between class labels and offers advantages in predictive performance and interpretability over traditional ”flat” classification. Despite its potential, its adoption in domains other than text, image and biology is limited, partly due to a perceived scarcity of suitable public datasets. This study performs an investigation into the availability of hierarchical datasets within the UCI Machine Learning Repository and OpenML.We employed a novel methodology using Large Language Models to automatically classify the metadata of over 1200 candidate datasets, followed by manual verification of promising candidates. Our findings reveal a shortage of public tabular datasets suitable for hierarchical classification. Out of the entire collection, only three potential datasets were identified. This work quantifies the data scarcity problem, highlighting it as a significant bottleneck that hinders research, development, and the broader application of hierarchical modeling techniques. To the best of our knowledge, this is the first large-scale quantitative study of hierarchical classification dataset availability in major public repositories.