Expanding Data-Agnostic Pivotal Instances Selection Models with Proximity Trees and Ensemble Learning
Summary
This paper proposes a hierarchical, interpretable-by-design pivot selection model based on proximity trees and ensemble learning. It is data-modality-agnostic and demonstrates competitive results across tabular, text, image, and time-series datasets.
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# Expanding Data-Agnostic Pivotal Instances Selection Models with Proximity Trees and Ensemble Learning Source: [https://arxiv.org/abs/2607.27522](https://arxiv.org/abs/2607.27522) [View PDF](https://arxiv.org/pdf/2607.27522) > Abstract:As decision\-making processes grow more complex, machine learning tools have become essential for tackling business and societal challenges\. However, many existing methods rely on decision\-making procedures that are difficult to interpret\. Since humans naturally make decisions by comparing new cases with a few representative examples, we aim to design an approach that selects such pivots to construct an interpretable predictive model\. Inspired by decision trees, we propose a hierarchical, interpretable\-by\-design pivot selection model based on the similarity between pivots and input instances\. Our method functions both as a pivot selection technique and a standalone predictive model\. Extending beyond single pivots, we incorporate pairs of pivots that are used by proximity and oblique trees, as well as ensembles, which enhance the versatility and effectiveness of our proposal\. Additionally, our approach is data modality\-agnostic, leveraging pre\-trained networks for data transformation\. Experiments across diverse datasets, including tabular data, text, images, and time series, demonstrate the effectiveness of our approach, outperforming alternative instance selection strategies and achieving competitive results against state\-of\-the\-art interpretable models while maintaining a minimal number of pivots\. ## Submission history From: Alessio Cascione \[[view email](https://arxiv.org/show-email/2c03b06a/2607.27522)\] **\[v1\]**Wed, 29 Jul 2026 23:23:01 UTC \(3,934 KB\)
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