We are happy to announce that the Paper “Online Random Feature Forests for Learning in Varying Feature Spaces” by Christian Schreckenberger, Yi He, Stefan Lüdtke, Christian Bartelt and Heiner Stuckenschmidt has been accepted for AAAI 2023 (Acceptance rate 19.6%)
We propose a new online learning algorithm tailored to data streams described by varying feature spaces (VFS), where new features can emerge constantly, and old features may stop to be observed over various time spans. Our proposed algorithm, named Online Random Feature Forests for Feature space Variabilities (ORF3V), provides a strategy to respect such feature dynamics by generating, updating, pruning, as well as online re-weighing an ensemble of what we call feature forests, which are generated and updated based on a compressed and storage efficient representation for each observed feature. We benchmark our algorithm on 12 datasets, including one novel real-world dataset of government COVID-19 responses collected through a crowd-sensing program in Spain. The empirical results substantiate the viability and effectiveness of our ORF3V algorithm and its superior accuracy performance over the state-of-the-art rival models.