近年来,机器学习模型在信用卡欺诈检测方面的应用日益加深,但受限于数据的敏感性和保密性,仍有较大提升空间。为实现更全面的个人客户画像,本研究创新性地引入反映个人经济状况、背景资料和交易信息的相关特征,针对信用卡交易中不平衡...近年来,机器学习模型在信用卡欺诈检测方面的应用日益加深,但受限于数据的敏感性和保密性,仍有较大提升空间。为实现更全面的个人客户画像,本研究创新性地引入反映个人经济状况、背景资料和交易信息的相关特征,针对信用卡交易中不平衡数据和欺诈检测问题,提出一种面向个人客户身份信息和交易特征的基于聚类下采样技术的混合神经网络模型(Hybrid Neural Network with Clustering-based Undersam-pling technique on Identity and Transaction features,HNN-CUHIT)。展开更多
A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductiv...A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.展开更多
文摘近年来,机器学习模型在信用卡欺诈检测方面的应用日益加深,但受限于数据的敏感性和保密性,仍有较大提升空间。为实现更全面的个人客户画像,本研究创新性地引入反映个人经济状况、背景资料和交易信息的相关特征,针对信用卡交易中不平衡数据和欺诈检测问题,提出一种面向个人客户身份信息和交易特征的基于聚类下采样技术的混合神经网络模型(Hybrid Neural Network with Clustering-based Undersam-pling technique on Identity and Transaction features,HNN-CUHIT)。
基金supported by the Fundamental Research Funds for the Central Universities (No.3122020072)the Multi-investment Project of Tianjin Applied Basic Research(No.23JCQNJC00250)。
文摘A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.
