Enriched Functional Tree-Based Classifiers: A Novel Approach Leveraging Derivatives and Geometric Features
This work addresses classification challenges in high-dimensional time series for fields like statistics and computer science, but it is incremental as it builds on existing functional and tree-based methods.
The researchers tackled the problem of classifying high-dimensional time series by introducing Enriched Functional Tree-Based Classifiers (EFTCs), which integrate Functional Data Analysis with tree-based ensemble methods using derivative and geometric features, resulting in improved predictive performance and reduced variance as demonstrated on real-world and simulated datasets.
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.