现代电子技术2024，Vol.47Issue(6)：124-130,7.DOI:10.16652/j.issn.1004-373x.2024.06.020
基于特征采样引导和集成RFELM的道路高排放源识别模型
On-road high-emitter identification model based on guided feature sampling and ensemble RFELM
摘要
Abstract
The pollution gas emitted by vehicles causes serious harm to the environment,among which the vehicles with excessive exhaust emissions are the major sources of pollutions.Therefore,it is of great significance to realize the effective identification of high-emitters on the road.A high-emitter identification model based on guided feature sampling and ensemble random Fourier feature extreme learning machines(RFELM)is proposed to classify the on-road remote sensing data.The remote sensing data is randomly sampled several times to construct multiple training subsets.Then,each training subset is sampled several times to train corresponding subclassifiers.The sampling probability and weight of feature are updated according to the input features of the optimal subclassifiers in the group.The validation scores of all subclassifiers are sorted,a certain proportion of RFELM is selected to form the classifier set,and the weighted voting method is used to predict the labels of the test data.The experimental results show that in comparison with RFELM,random forest and so on,the proposed model has better recognition performance and stronger noise resistance on real road remote sensing data.关键词
道路高排放源识别/遥测数据/特征采样/集成学习/随机傅里叶特征极限学习机/子分类器Key words
on-road high-emitter recognition/remote sensing data/feature sampling/ensemble learning/random Fourier feature extreme learning machine/subclassifier分类
电子信息工程引用本文复制引用
周汉胜,段培杰,李泽瑞,周金华..基于特征采样引导和集成RFELM的道路高排放源识别模型[J].现代电子技术,2024,47(6):124-130,7.基金项目
国家自然科学基金资助项目(62103125) （62103125）
国家自然科学基金资助项目(62033012) （62033012）
安徽省博士后研究人员科研活动资助项目(2021A484) （2021A484）
