Online Binary Space Partitioning Forests
This work addresses large-scale classification and regression problems by enabling online learning, though it appears incremental as it extends an existing method to an online setting.
The paper tackles the limitation of batch learning in Binary Space Partitioning-Tree processes by developing an online BSP-Forest framework, which achieves universal consistency and competitive performance on real-world datasets.
The Binary Space Partitioning-Tree~(BSP-Tree) process was recently proposed as an efficient strategy for space partitioning tasks. Because it uses more than one dimension to partition the space, the BSP-Tree Process is more efficient and flexible than conventional axis-aligned cutting strategies. However, due to its batch learning setting, it is not well suited to large-scale classification and regression problems. In this paper, we develop an online BSP-Forest framework to address this limitation. With the arrival of new data, the resulting online algorithm can simultaneously expand the space coverage and refine the partition structure, with guaranteed universal consistency for both classification and regression problems. The effectiveness and competitive performance of the online BSP-Forest is verified via simulations on real-world datasets.