论文标题
在线二进制空间划分森林
Online Binary Space Partitioning Forests
论文作者
论文摘要
最近提出了二进制空间划分-Tree（BSP-Tree）过程，作为空间分配任务的有效策略。由于它使用多个维度来分区空间，因此BSP-Tree过程比传统的轴线一致切割策略更有效，更灵活。但是，由于其批处理学习设置，它不太适合大规模分类和回归问题。在本文中，我们开发了一个在线BSP-ForeST框架来解决此限制。随着新数据的到来，由此产生的在线算法可以同时扩展空间覆盖范围并完善分区结构，并保证分类和回归问题的普遍一致性。通过现实世界数据集上的模拟验证了在线BSP-Forest的有效性和竞争性能。
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.
