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

TL;DR

This paper presents Enriched Functional Tree-Based Classifiers (EFTCs), a new method combining derivatives and geometric features within ensemble tree models for improved high-dimensional time series classification.

Contribution

It introduces EFTCs, integrating derivatives and geometric features into ensemble tree classifiers for enhanced performance in functional data classification.

Findings

EFTCs outperform traditional methods on real-world datasets.

Incorporating derivatives and geometric features improves classification accuracy.

Ensemble methods reduce variance and enhance predictive stability.

Abstract

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.