改进投影寻踪树分类器，提升高维复杂分类效果。
An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Improvements
- 放宽分裂限制，支持更灵活的投影寻踪分组。
- 在不均衡协方差结构下显著提升多类分类性能。
- 提供可视化工具验证算法有效性，适合算法评估者使用。
本文对投影寻踪树分类器进行改进，并提出可视化诊断方法以评估其在高维数据中的实际效果。原算法在树结构中限制深度小于类别数，导致对复杂分类问题过于僵化。新方法通过允许更多分裂和更灵活的类别分组，有效应对非线性分离与不等协方差结构下的多分类挑战。为验证改进的真实价值，我们设计两种可视化手段：利用高维可视化技术分析基准数据集上的模型拟合情况；并开发交互式网页应用，让用户在可控场景下对比原始与增强版分类器的行为。改进已实现于R包PPtreeExt中。
原文摘要 · Abstract (English)
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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