Neural Networks as Functional Classifiers
This work addresses the gap in using neural networks for functional data classification, which is incremental as it extends existing deep learning methods to a new domain.
The paper tackled the problem of applying deep learning to functional data for classification, demonstrating its effectiveness in applications like spectrographic data and showing performance gains over traditional methods like the functional linear model in simulations.
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.