An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Improvements

• URL: http://arxiv.org/abs/2602.21130v1
• Date: Tue, 24 Feb 2026 17:27:17 GMT
• Title: An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Improvements
• Authors: Natalia da Silva, Dianne Cook, Eun-Kyung Lee,
• Abstract summary: We develop visual diagnostic approaches to verify that the enhancements perform as intended.<n>An interactive web application enables users to explore the behavior of both the original and enhanced classifiers under controlled scenarios.
• Score: 0.3610948254980783
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Dynamic Perceiver for Efficient Visual Recognition [87.08210214417309]
  We propose Dynamic Perceiver (Dyn-Perceiver) to decouple the feature extraction procedure and the early classification task. A feature branch serves to extract image features, while a classification branch processes a latent code assigned for classification tasks. Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features.
  arXiv  Detail & Related papers  (2023-06-20T03:00:22Z)
• A Mathematical Programming Approach to Optimal Classification Forests [1.0499611180329806]
  This paper introduces Weighted Optimal Classification Forests (WOCFs)<n>WOCFs take advantage of an optimal ensemble of decision trees to derive accurate and interpretable classifiers.<n>Overall, WOCFs complement existing methods such as CART, Optimal Classification Trees, Random Forests and XGBoost.
  arXiv  Detail & Related papers  (2022-11-18T20:33:08Z)
• Improved Algorithms for Neural Active Learning [74.89097665112621]
  We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
  arXiv  Detail & Related papers  (2022-10-02T05:03:38Z)
• Supervised Dimensionality Reduction and Classification with Convolutional Autoencoders [1.1164202369517053]
  A Convolutional Autoencoder is combined to simultaneously produce supervised dimensionality reduction and predictions. The resulting Latent Space can be utilized to improve traditional, interpretable classification algorithms. The proposed methodology introduces advanced explainability regarding, not only the data structure through the produced latent space, but also about the classification behaviour.
  arXiv  Detail & Related papers  (2022-08-25T15:18:33Z)
• Fine-Grained Visual Classification using Self Assessment Classifier [12.596520707449027]
  Extracting discriminative features plays a crucial role in the fine-grained visual classification task. In this paper, we introduce a Self Assessment, which simultaneously leverages the representation of the image and top-k prediction classes. We show that our method achieves new state-of-the-art results on CUB200-2011, Stanford Dog, and FGVC Aircraft datasets.
  arXiv  Detail & Related papers  (2022-05-21T07:41:27Z)
• Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection [0.0]
  Two new algorithms are proposed to address the class overlap issue in the dataset. The proposed design is evaluated for both binary and multi-category classification.
  arXiv  Detail & Related papers  (2022-05-08T21:06:42Z)
• Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptation [25.114533037440896]
  We propose to extend the structure of two classifiers to multiple classifiers to further boost its performance. We demonstrate that, on average, adopting the structure of three classifiers normally yields the best performance as a trade-off between the accuracy and efficiency.
  arXiv  Detail & Related papers  (2021-08-02T03:00:13Z)
• Auto-weighted Multi-view Feature Selection with Graph Optimization [90.26124046530319]
  We propose a novel unsupervised multi-view feature selection model based on graph learning. The contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T03:25:25Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Dual Adversarial Auto-Encoders for Clustering [152.84443014554745]
  We propose Dual Adversarial Auto-encoder (Dual-AAE) for unsupervised clustering. By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders. Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods.
  arXiv  Detail & Related papers  (2020-08-23T13:16:34Z)
• AP-Loss for Accurate One-Stage Object Detection [49.13608882885456]
  One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously. The former suffers much from extreme foreground-background imbalance due to the large number of anchors. This paper proposes a novel framework to replace the classification task in one-stage detectors with a ranking task.
  arXiv  Detail & Related papers  (2020-08-17T13:22:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.