Speaker
Description
This talk 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, which is available on CRAN.
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https://claude.ai/ for grammar checking
Additional Material or Paper
The working paper is available at https://arxiv.org/abs/2602.21130 and we are in the final stages of revision prior to submission. The R package associated with this work, PPtreeExt, is available on CRAN (https://cran.rproject.org/web/packages/PPtreeExt/index.html).
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | classification, heterogeneous variance, machine learning, projection pursuit, separability, tree classifier |
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| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |