摘要
为研究航天员如何适应长期密闭环境对人体健康(生理、心理、精神)和机体功能的挑战,提出一种基于多标记学习的证候诊断模型。采用中医"望、闻、问、切"的方法,采集长期密闭环境下人体生命活动的状态数据,并运用数据挖掘方法研究、阐释其特点及变化规律。实验结果表明,该融合数据分类模型能达到80%的平均分类精度。
In order to study that spacemen how to adapt to challenges which longtime closed environment brings to human health（physiology,psychology,spirit）and bodily function,a differentiation model based on multi-label learning is proposed. This paper adopts＂inspection,auscultation and olfaction,inquiry and pulse-taking＂diagnose methods of Traditional Chinese Medicine（TCM）to collect human life activity state data in longtime closed environment. It uses data mining methods to study and explain its characteristics and varying patterns. Average precision of classification model built by fusion data reaches80% in the experiment.
出处
《计算机工程》
CAS
CSCD
2014年第9期13-18,22,共7页
Computer Engineering
基金
国家自然科学基金资助项目(61273305)
关键词
航天
密闭环境
中医
数据挖掘
特征选择
spaceflight
closed environment
Traditional Chinese Medicine（TCM）
data mining
feature selection
