Augmented Functional Random Forests: Classifier Construction and Unbiased Functional Principal Components Importance through Ad-Hoc Conditional Permutations
This work addresses classification problems in functional data analysis for researchers and practitioners dealing with high-dimensional correlated data, representing an incremental improvement over existing functional classifiers.
The paper tackles the challenge of classifying high-dimensional functional data by developing augmented functional classification trees and random forests that incorporate a new unbiased feature importance assessment tool for functional principal components. Experimental results show these methods significantly enhance predictive power compared to existing functional classifiers.
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.