华东理工大学学报（自然科学版）2017，Vol.43Issue(5)：698-703,6.DOI:10.14135/j.cnki.1006-3080.2017.05.015
基于极值随机森林的慢性胃炎中医证候分类
Syndrome Classification of Chronic Gastritis Based on Extremely Randomized Forest Algorithm
摘要
Abstract
Syndrome differentiation and treatment,which is the essence of traditional Chinese medicine (TCM),contain abundant rules.The majority of machine learning algorithms can obtain good classification accuracy,but these models are difficult to be explained.The models established by random forests have great interpretability,while these models cannot deal with multi-syndrome that patients may simultaneously have more than one syndrome in TCM.In this paper,syndrome classification for Chronic Gastritis (CG) is researched by using extremely randomized forest (ERF) algorithm,and compared with state-of-the-art multi-label algorithms and the tree-based algorithms (such as C4.5,CART).The experimental results show that ERF algorithm has better performance than other algorithms in the classification accuracy of every label and the six evaluation metrics of multi-label learning.The rules obtained in the model are basically in accord with TCM theory.关键词
证候分类/极值随机森林/可解释性/慢性胃炎/决策树Key words
syndrome classification/extremely randomized forest/interpretability/chronic gastritis/decision tree分类
医药卫生引用本文复制引用
颜建军,胡宗杰,刘国萍,王忆勤,付晶晶,郭睿,钱鹏..基于极值随机森林的慢性胃炎中医证候分类[J].华东理工大学学报（自然科学版）,2017,43(5):698-703,6.基金项目
国家自然科学基金(81270050,81302913,30901897,81173199) （81270050,81302913,30901897,81173199）
