基于改进蝶形反馈型神经网络的海关风险布控方法  

Customs risk control method based on improved butterfly feedback neural network

作　　者：王正刚 刘忠[1,2] 金瑾 刘伟 WANG Zhenggang;LIU Zhong;JIN Jin;LIU Wei(Chengdu Institution of Computer Application,Chinese Academy of Sciences,Chengdu Sichuan 610213,China;Graduate School,University of Chinese Academy of Sciences,Beijing 101408,China;Technology Department,Chengdu Customs District of The People’s Republic of China,Chengdu Sichuan 610041,China;Software Engineering School,Chengdu University of Information Technology,Chengdu Sichuan 610103,China)

机构地区：[1]中国科学院成都计算机应用研究所,成都610213 [2]中国科学院大学研究生院,北京101408 [3]中华人民共和国成都海关科技处,成都610041 [4]成都信息工程大学软件工程学院,成都610103

出　　处：《计算机应用》2023年第12期3955-3964,共10页journal of Computer Applications

基　　金：四川省科技厅创新人才支持计划项目(2020JDR0330)。

摘　　要：针对现阶段我国海关风险布控方法存在效率、准确率较低、人力资源占用过多的问题和智能化分类算法小型化部署需求,提出一种基于改进蝶形反馈型神经网络(BFNet-V2)的海关风险布控方法。首先,运用编码填充(FC)算法实现海关表格数据到模拟图像的语义替换;其次,运用BFNet-V2训练模拟图像数据,由左右两条链路、不同卷积核和块、小块的设计组成规则的神经网络结构,并添加残差短路径干预改善过拟合和梯度消失;最后,提出历史动量自适应矩估计算法(H-Adam)优化梯度下降过程,取得更优的自适应学习率调整方式,并分类海关数据。选取Xception(eXtreme inception)、移动网络(MobileNet)、残差网络(ResNet)和蝶形反馈型神经网络(BF-Net)为基线网络结构进行对比。BFNet-V2的接受者工作特征曲线(ROC)和查准率-查全率曲线(PR)包含了基线网络结构的曲线,与4种基线网络结构相比,基于迁移学习(TL)的BFNet-V2分类准确率分别提高了4.30%、4.34%、4.10%和0.37%。在真实标签数据分类过程中,BFNet-V2的查获误判率分别降低了70.09%、57.98%、58.36%和10.70%。比较所提方法与包含浅层和深度学习方法在内的8种分类方法,在3个数据集上的准确率均提升1.33%以上,可见所提方法能够实现表格数据自动分类,提升海关风险布控的效率和准确度。Aiming at the problems of low efficiency,low accuracy,excessive occupancy of human resources and intelligent classification algorithm miniaturization deployment requirements in China Customs risk control methods at this stage,a customs risk control method based on an improved Butterfly Feedback neural Network Version 2(BFNet-V2)was proposed.Firstly,the Filling in Code(FC)algorithm was used to realize the semantic replacement of the customs tabular data to the analog image.Then,the analog image data was trained by using the BFNet-V2.The regular neural network structure was composed of left and right links,different convolution kernels and blocks,and small block design,and the residual short path was added to improve the overfitting and gradient disappearance.Finally,a Historical momentum Adaptive moment estimation algorithm(H-Adam)was proposed to optimize the gradient descent process and achieve a better adaptive learning rate adjustment,and classify customs data.Xception(eXtreme inception),Mobile Network(MobileNet),Residual Network(ResNet),and Butterfly Feedback neural Network(BF-Net)were selected as the baseline network structures for comparison.The Receiver Operating Characteristic curve(ROC)and the Precision-Recall curve(PR)of the BFNet-V2 contain the curves of the baseline network structures.Taking Transfer Learning(TL)as an example,compared with the four baseline network structures,the classification accuracy of BFNet-V2 increases by 4.30%,4.34%,4.10%and 0.37%respectively.In the process of classifying real-label data,the misjudgment rate of BFNet-V2 reduces by 70.09%,57.98%,58.36%and 10.70%,respectively.The proposed method was compared with eight classification methods including shallow and deep learning methods,and the accuracies on three datasets increase by more than 1.33%.The proposed method can realize automatic classification of tabular data and improve the efficiency and accuracy of customs risk control.

关 键 词：卷积神经网络 模拟图像 自适应矩估计 海关 风险布控 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]