[1]耿家兴,万亚平,李洪飞.基于神经网络的混合数据的因果发现[J].计算机技术与发展,2020,30(05):26-31.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 006]
　GENG Jia-xing,WAN Ya-ping,LI Hong-fei.Causal Discovery of Mixed Data Based on Neural Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(05):26-31.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 006]
点击复制

基于神经网络的混合数据的因果发现()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年05期
页码:
26-31
栏目:
智能、算法、系统工程
出版日期:
2020-05-10

文章信息/Info

Title:
Causal Discovery of Mixed Data Based on Neural Network
文章编号:
1673-629X(2020)05-0026-06
作者:
耿家兴1 ; 万亚平1; 2 ; 李洪飞1
1. 南华大学 计算机学院,湖南 衡阳 421001; 2. 中核集团高可信计算重点学科实验室,湖南 衡阳 421001
Author(s):
GENG Jia-xing1 ; WAN Ya-ping1; 2 ; LI Hong-fei1
1. School of Computer Science,University of South China,Hengyang 421001,China; 2. CNNC Key Laboratory on High Trusted Computing,Hengyang 421001,China
关键词:
神经网络; 混合加性噪声; 因果推断; 梯度下降; HilberSchmidt 独立性
Keywords:
neural network; mixed additive noise; causal inference; gradient descent; HilberSchmidt independence
分类号:
TP181
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 05. 006
摘要:
因果推理正在成为机器学习领域一个越来越受关注的研究热点,现阶段的因果发现主要是在研究某一种假设条件下,基于纯粹的观测数据推断变量之间的因果方向。然而在现实世界中观察到的数据往往是由一些假设生成,使得传统因果推断方法的识别率不高、稳定性较差.针对当前的问题,提出了一种基于神经网络来解决混合数据因果推断的方法。该方法在混合加性噪声模型(ANM-MM) 的假设下,使用梯度下降法最优化改进的损失函数得到混合数据的抽象因果分布参数,然后将分布参数看作是原因变量和结果变量之间的隐变量, 通过比较原因变量和分布参数之间的 HilberSchmidt  独立性来确定二元变量的因果方向。 在理论上证明了该方法的可行性,并通过实验表明该算法在人工数据和真实数据的表现较传统的 IGCI,ANM,PNL,LiNGAM,SLOPE 方法具有较好的准确率和稳定性。
Abstract:
Causal discovery is becoming a research hotspot in the field of machine learning. At present,the causal discovery is mainly to investigate the causal direction between variables based on pure observation data under the study of a certain assumption. However,the data observed in the real world is often generated by some assumptions,which makes the traditional causal inference method less accurate and less stable. Aiming at the current problem,a method based on neural network to solve the causal inference of mixed data is proposed. Under the assumption of additive noise model-mixture model (ANM-MM) ,the gradient loss method is used to optimize the improved loss function to obtain the abstract causal distribution parameters of the mixed data,and then the distribution parameters are regarded as hidden variable between the cause variable and the result variable. The hidden variable determines the causal direction of the binary variable by comparing the HilberSchmidt independence between the causal variable and the distribution parameter. The feasibility of the method is proved theoretically. The experiment shows that the proposed algorithm has better accuracy and stability than the traditional methods like IGCI,ANM,PNL,LiNGAM and SLOPE.

相似文献/References:

[1]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
　LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2010,(05):84.
[2]高峥 陈蜀宇 李国勇.混合入侵检测系统的研究[J].计算机技术与发展,2010,(06):148.
　GAO Zheng,CHEN Shu-yu,LI Guo-yong.Research of a Hybrid Intrusion Detection System[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2010,(05):148.
[3]包力伟 周俊.铸锻企业生产质量控制系统的开发[J].计算机技术与发展,2008,(04):174.
　BAO Li-wei,ZHOU Jun.Development of a Manufacture Quality Control System in Casting Company[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2008,(05):174.
[4]李志俊 程家兴 金奎 饶玉佳.基于样本期望训练数的BP神经网络改进研究[J].计算机技术与发展,2009,(05):103.
　LI Zhi-jun,CHENG Jia-xing,JIN Kui,et al.BP Algorithm Improvement Based on Sample Expected Training Number[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2009,(05):103.
[5]李龙澍 葛瑞峰 王慧萍.基于神经网络的批强化学习在Robocup中的应用[J].计算机技术与发展,2009,(07):98.
　LI Long-shu,GE Rui-feng,WANG Hui-ping.Application of Batch Reinforcement Learning Based on NN to Robocup[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2009,(05):98.
[6]贾志先.神经网络在空白试卷识别中的应用[J].计算机技术与发展,2009,(08):208.
　JIA Zhi-xian.Application of Neural Network in Recognization Blank Examination Paper[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2009,(05):208.
[7]肖宜龙 路游 亓永刚.基于神经网络的NURBS曲面重建[J].计算机技术与发展,2009,(09):65.
　XIAO Yi-long,LU You,QI Yong-gang.NURBS Surface Reconstruction Based on Neural Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2009,(05):65.
[8]蔡秋茹 罗烨 柳益君 叶飞跃.企业资信的BP神经网络评估模型研究[J].计算机技术与发展,2009,(10):117.
　CAI Qiu-ru,LUO Ye,LIU Yi-jun,et al.Research on BP Neural Network Model for Corporation Credit Rating[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2009,(05):117.
[9]王晓敏 刘希玉 戴芬.BP神经网络预测算法的改进及应用[J].计算机技术与发展,2009,(11):64.
　WANG Xiao-min,LIU Xi-yu,DAI Fen.Improvement and Application of BP Neural Network Forecasting Algorithm[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2009,(05):64.
[10]崔海青 刘希玉.基于粒子群算法的RBF网络参数优化算法[J].计算机技术与发展,2009,(12):117.
　CUI Hai-qing,LIU Xi-yu.Parameter Optimization Algorithm of RBF Neural Network Based on PSO Algorithm[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2009,(05):117.

更新日期/Last Update: 2020-05-10