基于堆叠集成算法的质量分类案例分析
Case Analysis of Quality Classification Based on Stacked Ensemble Algorithm
常凤 1刘静 1胡忠旭 1艾鹏1
作者信息
摘要
阐述针对葡萄酒品质分类常用的单一算法,提出堆叠集成算法,通过参数优化SVM、GBDT、RF、KNN学习器,将结果作为元学习器的RF输入特征.实验证明,堆叠集成算法评价指标显著提高.
Abstract
This paper describes the commonly used single algorithm for wine quality classification,proposes a stacked ensemble algorithm,optimizes SVM,GBDT,RF,KNN learners through parameters,and uses the results as the RF input features of the meta learner.Experimental results have shown that the evaluation metrics of stacked ensemble algorithms have significantly improved.
关键词
堆叠集成算法/元学习器/基学习器Key words
stacking ensemble algorithm/meta learner/base learner引用本文复制引用
出版年
2024
NETL
NSTL
万方数据