Supervised Learning via Ensembles of Diverse Functional Representations: the Functional Voting Classifier

arXiv:2403.15778v1 Announce Type: cross
Abstract: 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 …

abstract analysis arxiv challenges classifier cs.lg data data analysis diverse face fda framework functional functions machine machine learning modeling nature statistical stat.me stat.ml supervised learning temporal type via voting