引入二阶马尔可夫假设的逻辑回归异质性网络分类方法

doi:10.13229/j.cnki.jdxbgxb20170717

吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (5): 1571-1577.doi: 10.13229/j.cnki.jdxbgxb20170717

引入二阶马尔可夫假设的逻辑回归异质性网络分类方法

董飒^1,2, 刘大有^1,2, 欧阳若川³, 朱允刚^1,2, 李丽娜^1,2

1.吉林大学计算机科学与技术学院,长春 130012;
2.吉林大学符号计算与知识工程教育部重点实验室,长春 130012;
3.吉林大学大数据和网络管理中心,长春 130012

收稿日期:2017-07-10 出版日期:2018-09-20 发布日期:2018-12-11
通讯作者: 刘杰(1973-),女,副教授.研究方向:数据挖掘,模式识别.E-mail:liu_jie@jlu.edu.cn
作者简介:董飒(1985-),女,工程师,博士研究生.研究方向:人工智能,数据挖掘.E-mail:dongsa7701@163.com
基金资助:
国家自然科学基金项目 (61502198, 61572226, 61472161, 61373053);吉林省科技发展计划项目 (20150520066JH);应用光学国家重点实验室开放基金项目

Logistic regression classification in networked data with heterophily based on second-order Markov assumption

DONG Sa^1,2, LIU Da-you^1,2, OUYANG Ruo-chuan³, ZHU Yun-gang^1,2, LI Li-na^1,2

1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
3.Big Data and Network Management Center, Jilin University, Changchun 130012, China

Received:2017-07-10 Online:2018-09-20 Published:2018-12-11

摘要/Abstract

摘要： 针对异质性网络的分类问题提出了一种引入二阶马尔可夫假设链接的逻辑回归分类方法。该方法采用二阶马尔可夫链接,扩展结点邻居的邻居之链接特征(类分布)用于构造结构化的逻辑回归模型,并与基于一阶马尔可夫假设的逻辑回归模型相结合,利用松弛标注的协作推理方法逐步更新类分布至最终分类结果。对比实验结果表明,本文方法在异质性网络分类上分类精度较佳。

关键词: 人工智能, 异质性网络分类, 结构化逻辑回归模型, 网络数据

Abstract: A logistic regression classifier based on second-order Markov assumption is proposed for heterophilous network. The algorithm employs the second-order Markov links, extends neighbors' link distributions of node neighbors in order to construct structured logistic regression model. At the same time it combines the first-order Markov logistic regression model to update the class distributions progressively using relaxation labeling collective inference method. Comparison of experiment results shows that the enhanced algorithm performs better on the heterophilious network.

Key words: artificial intelligence, heterophilous network classification, structured logistic regression model, networked data

中图分类号:

TP301

董飒, 刘大有, 欧阳若川, 朱允刚, 李丽娜. 引入二阶马尔可夫假设的逻辑回归异质性网络分类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1571-1577.

DONG Sa, LIU Da-you, OUYANG Ruo-chuan, ZHU Yun-gang, LI Li-na. Logistic regression classification in networked data with heterophily based on second-order Markov assumption[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1571-1577.

参考文献

[1] Mcdowell L K, Aha D W.Leveraging neighbor attributes for classification in sparsely labeled networks[J].ACM Transactions on Knowledge Discovery from Data,2016,11(1):1-37.
[2] Li M, Pan S R, Zhang Y, et al.Classifying networked text data with positive and unlabeled examples[J]. Pattern Recognition Letters,2016,77:1-7.
[3] King J R, Mcdowell L K.Correcting relational bias to improve classification in sparsely-labeled Networks[C]∥Proceedings of 3rd IEEE/ACM International Conference on Data Science and Advanced Analytics,Montral, Canada,2016:31-40.
[4] Macskassy S A, Provost F.A simple relational classifie[C]∥Proceedings of the Multi-Relational Data Mining Workshop at the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,New York,2003:24-27.
[5] Perlich C,Provost F.Distribution-based aggregation for relational leaning with identifier attributes[J]. Machine Learning, 2006, 62(1/2):65-105.
[6] Perlich C, Provost F.Aggregation-based feature invention and relational concept classes[C]∥Proceedings of 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington DC,2003:167-176.
[7] Lu Q, Getoor L.Link-based classification[C]∥Proceedings of 12th International Conference on Machine Learning (ICML), Washington DC,2003:496-503.
[8] Chakrabarti S, Dom B, Indyk P.Enhanced hypertext categorization using hyperlinks[C]∥Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, New York, USA, 1998:307-319.
[9] Lin F, Cohen W W.Semi-supervised classification of network data using very few labels[C]∥Proceedings of the International Conference on Advances in Social Network Analysis and Mining, IEEE Computer Society,Washington,DC,USA,2010:192-199.
[10] Macskassy S A, Provost F.Classification in networked data: a toolkit and a univariate case study[J].Journal of Machine Learning Research, 2007, 8(3):935-983.
[11] McPherson M, Smith-Lovin L, Cook J M. Birds of a feather: homophily in social networks[J].Annual Review of Sociology,2001,27:415-444.
[12] Wang Zhen-wen, Yin Feng-jing, Tan Wen-tang, et al.Classification in networked data with heterophily[J].The Scientific World Journal,2013,2013(2):236769.
[13] 董飒,刘大有,李丽娜,等. 基于类传播分布的关系近邻异质性网络分类方法[J].吉林大学学报:工学版,2016,46(2):522-527.
Dong Sa, Liu Da-you, Li Li-na, et al.Relational neighbor algorithm based on class propagation distributions for classification in networked data with heterophily[J].Journal of Jilin University(Engineering and Technology Edition),2016,46(2):522-527.
[14] Gupta M, Kumar P, Bhasker B.HeteClass: a meta-path based framework for transductive classification of objects in heterogeneous information networks[J]. Expert Systems with Applications,2017,68:106-122.
[15] 李丽娜. 基于链接的网络分类和链接预测新方法研究[D].长春:吉林大学计算机科学与技术学院,2012.
Li Li-na.Research on classification and link prediction of network data based on links[D]. Changchun: College of Computer Science and Technology,Jilin University, 2012.
[16] 林金花, 王延杰. 三维语义场景复原网络[J]. 光学精密工程, 2018, 26(5): 1231-1241.
Lin Jin-hua, Wang Yan-jie.Three-dimentional reconstruction of semantic scene based on RGB-D map[J]. Optics and Precision Engineering,2018,26(5):1231-1241.
[17] Geman S, Geman D.Stochastic relaxation, Gibbs distributions and Bayesian restoration of images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1984(6):721-741.
[18] Zhang T, Oles F J.Text categorization based on regularized linear classification methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,4(1):5-13.
[19] Le Cessie S, van Houwelingen J C. Ridge estimators in logistic regression[J].Applied Statistics,1992,41(1):191-201.
[20] 高瞻宇, 顾营迎, 刘宇航, 等. 采用简化Brown模型及改进BFGS法的相机自标定[J]. 光学精密工程, 2017, 25(9): 2532-2540.
Gao Zhan-yu, Gu Ying-ying, Liu Yu-hang, et al.Self-calibration based on simplified brown non-linear camera model and modified BFGS algorithm[J]. Optics and Precision Engineering, 2017, 25(9): 2532-2540.
[21] Sen P, Namata G M, Bilgic M, et al.Collective classication in network data[J].AI Magazine,2008,29(3):93-106.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

引入二阶马尔可夫假设的逻辑回归异质性网络分类方法

Logistic regression classification in networked data with heterophily based on second-order Markov assumption

RICH HTML

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	顾海军, 田雅倩, 崔莹. 基于行为语言的智能交互代理[J]. 吉林大学学报(工学版), 2018, 48(5): 1578-1585.
[2]	王旭, 欧阳继红, 陈桂芬. 基于垂直维序列动态时间规整方法的图相似度度量[J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[3]	张浩, 占萌苹, 郭刘香, 李誌, 刘元宁, 张春鹤, 常浩武, 王志强. 基于高通量数据的人体外源性植物miRNA跨界调控建模[J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[4]	黄岚, 纪林影, 姚刚, 翟睿峰, 白天. 面向误诊提示的疾病-症状语义网构建[J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[5]	李雄飞, 冯婷婷, 骆实, 张小利. 基于递归神经网络的自动作曲算法[J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[6]	刘杰, 张平, 高万夫. 基于条件相关的特征选择方法[J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[7]	王旭, 欧阳继红, 陈桂芬. 基于多重序列所有公共子序列的启发式算法度量多图的相似度[J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[8]	杨欣, 夏斯军, 刘冬雪, 费树岷, 胡银记. 跟踪-学习-检测框架下改进加速梯度的目标跟踪[J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[9]	刘雪娟, 袁家斌, 许娟, 段博佳. 量子k-means算法[J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[10]	曲慧雁, 赵伟, 秦爱红. 基于优化算子的快速碰撞检测算法[J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[11]	李嘉菲, 孙小玉. 基于谱分解的不确定数据聚类方法[J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[12]	邵克勇, 陈丰, 王婷婷, 王季驰, 周立朋. 无平衡点分数阶混沌系统全状态自适应控制[J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
[13]	王生生, 王创峰, 谷方明. OPRA方向关系网络的时空推理[J]. 吉林大学学报(工学版), 2017, 47(4): 1238-1243.
[14]	马淼, 李贻斌. 基于多级图像序列和卷积神经网络的人体行为识别[J]. 吉林大学学报(工学版), 2017, 47(4): 1244-1252.
[15]	周炳海, 彭涛. 混流装配线准时化物料配送调度优化[J]. 吉林大学学报(工学版), 2017, 47(4): 1253-1261.