Improving functional classification performance through diversity: the functional voting approach

In recent decades, Functional Data Analysis (FDA) has become widely popular as a framework for ana- lyzing data that are inherently 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. The focal point of this study 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 diversity can increase predictive accuracy. 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.