Online Binary Space Partitioning Forests

Xuhui Fan, Bin Li, Scott SIsson
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:527-537, 2020.

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 cut 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 classification problems. The effectiveness and competitive performance of the online BSP-Forest is verified via simulations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-fan20a, title = {Online Binary Space Partitioning Forests}, author = {Fan, Xuhui and Li, Bin and SIsson, Scott}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {527--537}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/fan20a/fan20a.pdf}, url = {https://proceedings.mlr.press/v108/fan20a.html}, 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 cut 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 classification problems. The effectiveness and competitive performance of the online BSP-Forest is verified via simulations.} }
Endnote
%0 Conference Paper %T Online Binary Space Partitioning Forests %A Xuhui Fan %A Bin Li %A Scott SIsson %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-fan20a %I PMLR %P 527--537 %U https://proceedings.mlr.press/v108/fan20a.html %V 108 %X 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 cut 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 classification problems. The effectiveness and competitive performance of the online BSP-Forest is verified via simulations.
APA
Fan, X., Li, B. & SIsson, S.. (2020). Online Binary Space Partitioning Forests. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:527-537 Available from https://proceedings.mlr.press/v108/fan20a.html.

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