# Enriched Functional Tree-Based Classifiers: A Novel Approach Leveraging Derivatives and Geometric Features

arXiv: https://arxiv.org/abs/2409.17804 · HTML: https://mlsanity.com/paper/2409.17804/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Grouped variable importance with random forests and application to multiple functional data analysis](https://mlsanity.com/paper/1411.4170/) (2014)
- [Supervised Learning via Ensembles of Diverse Functional Representations: the Functional Voting Classifier](https://mlsanity.com/paper/2403.15778/) (2024)
- [Classification of multivariate functional data on different domains with Partial Least Squares approaches](https://mlsanity.com/paper/2212.09145/) (2022)
- [Mixture of segmentation for heterogeneous functional data](https://mlsanity.com/paper/2303.10712/) (2023)
- [Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series](https://mlsanity.com/paper/2409.07879/) (2024)

## Abstract (arXiv)

The positioning of this research falls within the scalar-on-function classification literature, a field of significant interest across various domains, particularly in statistics, mathematics, and computer science. This study introduces an advanced methodology for supervised classification by integrating Functional Data Analysis (FDA) with tree-based ensemble techniques for classifying high-dimensional time series. The proposed framework, Enriched Functional Tree-Based Classifiers (EFTCs), leverages derivative and geometric features, benefiting from the diversity inherent in ensemble methods to further enhance predictive performance and reduce variance. While our approach has been tested on the enrichment of Functional Classification Trees (FCTs), Functional K-NN (FKNN), Functional Random Forest (FRF), Functional XGBoost (FXGB), and Functional LightGBM (FLGBM), it could be extended to other tree-based and non-tree-based classifiers, with appropriate considerations emerging from this investigation. Through extensive experimental evaluations on seven real-world datasets and six simulated scenarios, this proposal demonstrates fascinating improvements over traditional approaches, providing new insights into the application of FDA in complex, high-dimensional learning problems.