摘要
为了对航空电子设备的测试数据进行有效约简,去除冗余信息和不相关特征,基于机器学习领域现有的特征选择算法,提出了一种元学习框架下的航空电子设备特征选择算法推荐方法。所提方法旨在根据不同航空电子设备测试数据所蕴含的信息,推荐合适的特征选择算法。首先,分析了数据集特征的描述方法。然后,介绍了采用综合度量指数的算法性能评价方法。最后,给出了特征选择算法推荐方法的框架。使用42个航空电子设备的测试数据和13个过滤型特征选择算法建立了元数据库,采用留一法进行交叉验证,推荐命中率达到了90%以上,推荐性能比例达到97%以上。
In order to reduce the test data of avionics effectively and remove redundant information and irrelevant features,based on the existing feature selection algorithms in the field of machine learning,a recommendation method for avionics feature selection algorithm under the meta-learning framework is proposed.The proposed method aims to recommend appropriate feature selection algorithms according to the information contained in the test data of different avionics.Firstly,the description method of data set feature is analyzed.Then,the algorithm performance evaluation method based on the multi-metric index is introduced.Finally,the framework of recommendation method for feature selection algorithm is given.A metadata database is established on 42 avionics data sets and 13 filtering feature selection algorithms,the leave-one-out method is used for cross validation.The recommended hit radio reaches more than 90%and the recommended performance radio reaches more than 97%.
作者
李睿峰
许爱强
孙伟超
王树友
LI Ruifeng;XU Aiqiang;SUN Weichao;WANG Shuyou(Naval Aviation University, Yantai 264001, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2021年第7期2011-2020,共10页
Systems Engineering and Electronics
基金
军内科研项目(4172122113R)资助课题。
关键词
故障诊断
元学习
特征选择
算法推荐
航空电子设备
fault diagnosis
meta-learning
feature selection
algorithm recommendation
avionics
