Online Binary Space Partitioning Forests

Xuhui Fan, Bin Li, Scott A. Sisson

Research output: Contribution to journalConference paperpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)527-536
Number of pages10
JournalProceedings of Machine Learning Research
Volume108
Publication statusPublished - 2020
Externally publishedYes
Event23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020 - Virtual, Online
Duration: 26 Aug 202028 Aug 2020

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