基于粗糙集的决策树在粮情监控系统中的应用  

Application of decision tree based on rough set in grain situation monitoring system

作　　者：郭利进[1] 衣美佳 GUO Li-jin;YI Mei-jia(School of Electrical Engineering and Automation,Tianjin Polytechnic University,Tianjin,300387,China)

机构地区：[1]天津工业大学电气工程及其自动化学院,天津300387

出　　处：《粮食与油脂》2021年第9期91-94,共4页Cereals & Oils

摘　　要：对某粮仓采集到的数据进行仿真试验,将单棵C4.5决策树、随机森林、经过属性约简后的随机森林进行比较。结果表明经属性约简后的随机森林提高了计算精度且减少了时间消耗。A simulation experiment was carried out on the data collected in a granary,and a single C4.5 decision tree was compared with a random forest,a random forest after attribute reduction. It showed that the random forest after attribute reduction improved the calculation accuracy and reduced the time consumption.

关 键 词：粗糙集 属性约简 C4.5算法 随机森林 

分 类 号：TS205[轻工技术与工程—食品科学]