Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series
函数型数据分类的随机样条树:理论与环境时间序列应用
Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducing variance in ensemble methods.This paper introduces Randomized Spline Trees (RST), a novel algorithm that bridges these two approaches by incorporating randomized functional representations into the Random Forest framework. RST generates diverse functional…