稀疏投影斜随机森林
Sparse Projection Oblique Randomer Forests
AI总结：
SPORF是一种使用稀疏随机投影的斜决策森林，在超过100个分类基准问题上显著提升准确性，同时保持可解释性和计算效率。
摘要：
决策森林，包括随机森林和梯度提升树，近年来在各种机器学习环境中展示了最先进的性能。决策森林通常是轴对齐决策树的集成；也就是说，树仅沿特征维度进行分裂。相比之下，许多决策森林的最新扩展基于轴斜分裂。不幸的是，这些扩展牺牲了基于轴对齐分裂的决策森林的一个或多个有利特性，例如对许多噪声维度的鲁棒性、可解释性或计算效率。我们引入了另一种决策森林，称为“稀疏投影斜随机森林”（SPORF）。SPORF使用非常稀疏的随机投影，即一小部分特征的线性组合。在一个包含超过100个不同维度、样本量和类别数问题的标准分类基准套件上，SPORF显著提高了相对于现有最先进算法的准确性。为了说明SPORF如何解决轴对齐和现有斜决策森林方法的局限性，我们进行了广泛的模拟实验。SPORF通常比现有决策森林产生改进的性能，同时缓解计算效率和可扩展性问题并保持可解释性。SPORF可以轻松地纳入其他集成方法，如提升，以获得潜在的类似收益。
英文摘要：
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many recent extensions to decision forests are based on axis-oblique splits. Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency. We introduce yet another decision forest, called "Sparse Projection Oblique Randomer Forests" (SPORF). SPORF uses very sparse random projections, i.e., linear combinations of a small subset of features. SPORF significantly improves accuracy over existing state-of-the-art algorithms on a standard benchmark suite for classification with >100 problems of varying dimension, sample size, and number of classes. To illustrate how SPORF addresses the limitations of both axis-aligned and existing oblique decision forest methods, we conduct extensive simulated experiments. SPORF typically yields improved performance over existing decision forests, while mitigating computational efficiency and scalability and maintaining interpretability. SPORF can easily be incorporated into other ensemble methods such as boosting to obtain potentially similar gains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
