  Many conventional statistical and machine learning methods face challenges
when applied directly to high dimensional temporal observations. In recent
decades, Functional Data Analysis (FDA) has gained widespread popularity as a
framework for modeling and analyzing data that are, by their nature, functions
in the domain of time. Although supervised classification has been extensively
explored in recent decades within the FDA literature, ensemble learning of
functional classifiers has only recently emerged as a topic of significant
interest. Thus, the latter subject presents unexplored facets and challenges
from various statistical perspectives. The focal point of this paper lies in
the realm of ensemble learning for functional data and aims to show how
different functional data representations can be used to train ensemble members
and how base model predictions can be combined through majority voting. The
so-called Functional Voting Classifier (FVC) is proposed to demonstrate how
different functional representations leading to augmented diversity can
increase predictive accuracy. Many real-world datasets from several domains are
used to display that the FVC can significantly enhance performance compared to
individual models. The framework presented provides a foundation for voting
ensembles with functional data and can stimulate a highly encouraging line of
research in the FDA context.
