一种增强的投影追求树分类器及其用于评估算法改进的可视化方法
An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Improvements
AI总结：
本文改进了投影追求树分类器并提出可视化方法评估其在高维数据中的性能，通过更灵活的分裂和分类分组提升多类分类效果。
摘要：
本文提出了投影追求树分类器的改进方法，并开发了可视化诊断方法以评估其在高维数据中的影响。原始算法使用变量线性组合在树结构中，深度限制为小于类别数，这一限制在复杂分类问题中过于僵化。我们的扩展通过允许更多分裂和更灵活的分类分组，提高了多类设置中方差-协方差结构不等和非线性类分离的性能。提出算法改进是直接的，但证明其实际效用却非易事。因此，我们开发了两种可视化诊断方法来验证改进的性能。利用高维可视化技术，我们检查模型在基准数据集上的拟合情况以评估算法行为是否如理论所预测。一个交互式网页应用使用户能够在受控场景下探索原始和改进分类器的行为。这些改进通过R包PPtreeExt实现。
英文摘要：
This paper presents enhancements to the projection pursuit tree classifier and visual diagnostic methods for assessing their impact in high dimensions. The original algorithm uses linear combinations of variables in a tree structure where depth is constrained to be less than the number of classes -- a limitation that proves too rigid for complex classification problems. Our extensions improve performance in multi-class settings with unequal variance-covariance structures and nonlinear class separations by allowing more splits and more flexible class groupings in the projection pursuit computation. Proposing algorithmic improvements is straightforward; demonstrating their actual utility is not. We therefore develop two visual diagnostic approaches to verify that the enhancements perform as intended. Using high-dimensional visualization techniques, we examine model fits on benchmark datasets to assess whether the algorithm behaves as theorized. An interactive web application enables users to explore the behavior of both the original and enhanced classifiers under controlled scenarios. The enhancements are implemented in the R package PPtreeExt.
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