Neural Networks as Functional Classifiers

TL;DR

This paper extends deep learning techniques to functional data classification, demonstrating their effectiveness on spectrographic data and outperforming traditional models in simulations.

Contribution

It introduces a novel application of neural networks for functional data classification, bridging a gap in existing machine learning approaches.

Findings

Neural networks outperform traditional models in functional data classification.

The method is effective on spectrographic data.

Simulation studies show improved accuracy over existing models.

Abstract

In recent years, there has been considerable innovation in the world of predictive methodologies. This is evident by the relative domination of machine learning approaches in various classification competitions. While these algorithms have excelled at multivariate problems, they have remained dormant in the realm of functional data analysis. We extend notable deep learning methodologies to the domain of functional data for the purpose of classification problems. We highlight the effectiveness of our method in a number of classification applications such as classification of spectrographic data. Moreover, we demonstrate the performance of our classifier through simulation studies in which we compare our approach to the functional linear model and other conventional classification methods.