Scholarlink

Functional Data Analysis (FDA) has become popular in the statistical literature for modelling high-dimensional time series. Although supervised learning has been broadly explored from various perspectives, ensembles of functional classifiers have only lately emerged as a matter of substantial interest. The latter topic offers novel aspects and challenges from mixed statistical viewpoints. This article focuses on ensemble learning for functional data and offers a possible approach where distinct functional representations can be adopted to train ensemble members, and base-model predictions can be combined to improve classifiers’ performances.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Supervised Classification of Functional Data via Ensembles of Different Functional Representations

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Donato Riccio,
  • Fabrizio Maturo,
  • Elvira Romano

摘要

Functional Data Analysis (FDA) has become popular in the statistical literature for modelling high-dimensional time series. Although supervised learning has been broadly explored from various perspectives, ensembles of functional classifiers have only lately emerged as a matter of substantial interest. The latter topic offers novel aspects and challenges from mixed statistical viewpoints. This article focuses on ensemble learning for functional data and offers a possible approach where distinct functional representations can be adopted to train ensemble members, and base-model predictions can be combined to improve classifiers’ performances.