摘要
为解决传统选取航材品种方法对人的经验依赖较多、准确率不高、运算效率低等问题,提出一种采用XGboost算法的携行航材品种选择方法。建立分类特征体系,使用XGboost算法对特征进行重要性排序、分析和筛选,构建精简版分类特征体系,使用K折交叉验证法和经验调参对样本数据分组和训练,并与GBDT、RF、Adaboost等分类算法的结果比析。结果表明:XGboost算法可减少人为因素干预,在携行航材品种确定应用中具有高效性、科学性和优越性。
In order to solve these problems of traditional selecting aircraft spare parts method,such as more relying on human experience,low accuracy and low operational efficiency,a new method of selecting aircraft spare parts by using XGboost algorithm was proposed.The classification feature system was established.The XGboost algorithm was used to sort,analyze and screen the features,and the simplified version of the classification feature system was constructed.The k-fold cross validation method and empirical reference were used to group and train the sample data,and the results were compared with GBDT,RF,Adaboost and other classification algorithms.The results show that the XGboost algorithm can reduce the interference of human factors,and it is efficient,scientific and optimal.
作者
宋传洲
陈育良
杨宜霖
Song Chuanzhou;Chen Yuliang;Yang Yilin(School of Coast Guard,Navy Aviation University,Yantai 264001,China;No.94679 Unit of PLA,Nanjing 210000,China)
出处
《兵工自动化》
2021年第2期75-80,共6页
Ordnance Industry Automation
关键词
携行航材
品种确定
XGboost
分类
面向任务
portable aircraft spare parts
variety determination
XGboost
classification
mission oriented
