Online Hierarchical Partitioning of the Output Space in Extreme Multi-Label Data Streams
Published in European Conference on Artificial Intelligence (ECAI), 2025
This paper presents iHOMER, a novel framework for online multi-label learning in dynamic environments. iHOMER is the first incremental, drift-aware algorithm that partitions the label space into correlated clusters without requiring predefined hierarchies. It adapts to concept drift over time, balances scalability and predictive performance, and employs statistical tests to guide both splitting and merging of label clusters.
Recommended citation: Neves, L., Lourenço, A., Cano, A., & Marreiros, G. (2025). Online hierarchical partitioning of the output space in extreme multi-label data streams. GECAD, ISEP, Polytechnic of Porto; Virginia Tech.
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