# Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series

arXiv: https://arxiv.org/abs/2409.07879 · HTML: https://mlsanity.com/paper/2409.07879/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 12 Sep 2024.

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

- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Diversity in Machine Learning](https://mlsanity.com/paper/1807.01477/) (2018)
- [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)

## Built on by (most cited first, 1 of 1)

- [Enriched Functional Tree-Based Classifiers: A Novel Approach Leveraging Derivatives and Geometric Features](https://mlsanity.com/paper/2409.17804/) (2024)

## Abstract (arXiv)

Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducing variance in ensemble methods.This paper introduces Randomized Spline Trees (RST), a novel algorithm that bridges these two approaches by incorporating randomized functional representations into the Random Forest framework. RST generates diverse functional representations of input data using randomized B-spline parameters, creating an ensemble of decision trees trained on these varied representations. We provide a theoretical analysis of how this functional diversity contributes to reducing generalization error and present empirical evaluations on six environmental time series classification tasks from the UCR Time Series Archive. Results show that RST variants outperform standard Random Forests and Gradient Boosting on most datasets, improving classification accuracy by up to 14%. The success of RST demonstrates the potential of adaptive functional representations in capturing complex temporal patterns in environmental data. This work contributes to the growing field of machine learning techniques focused on functional data and opens new avenues for research in environmental time series analysis.