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.

功能数据分析（FDA）是用于建模和分析时间域函数数据的框架，在该领域中，集成学习的功能分类器只是最近引起了极大的兴趣。该论文的重点是基于功能数据的集成学习，展示了如何使用不同的功能表示来训练集成成员，并通过多数投票来组合基模型的预测结果。所提出的功能投票分类器（FVC）证明了不同的功能表示可以增加多样性从而提高预测准确性。使用多个真实世界数据集显示，与单个模型相比，FVC 可以显著提高性能。这个框架为功能数据的投票集成提供了基础，并在 FDA 领域激发了一个非常有潜力的研究方向。