# Augmented Functional Random Forests: Classifier Construction and Unbiased Functional Principal Components Importance through Ad-Hoc Conditional Permutations

arXiv: https://arxiv.org/abs/2408.13179 · HTML: https://mlsanity.com/paper/2408.13179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Grouped variable importance with random forests and application to multiple functional data analysis](https://mlsanity.com/paper/1411.4170/) (2014)
- [Functional Data Analysis: An Introduction and Recent Developments](https://mlsanity.com/paper/2312.05523/) (2023)
- [Supervised Learning via Ensembles of Diverse Functional Representations: the Functional Voting Classifier](https://mlsanity.com/paper/2403.15778/) (2024)
- [Random Survival Forest for Censored Functional Data](https://mlsanity.com/paper/2407.15340/) (2024)

## Abstract (arXiv)

This paper introduces a novel supervised classification strategy that integrates functional data analysis (FDA) with tree-based methods, addressing the challenges of high-dimensional data and enhancing the classification performance of existing functional classifiers. Specifically, we propose augmented versions of functional classification trees and functional random forests, incorporating a new tool for assessing the importance of functional principal components. This tool provides an ad-hoc method for determining unbiased permutation feature importance in functional data, particularly when dealing with correlated features derived from successive derivatives. Our study demonstrates that these additional features can significantly enhance the predictive power of functional classifiers. Experimental evaluations on both real-world and simulated datasets showcase the effectiveness of the proposed methodology, yielding promising results compared to existing methods.