基于用户身份特征的多标签分类算法

doi:10.11772/j.issn.1001-9081.2017.06.1697

计算机应用 ›› 2017, Vol. 37 ›› Issue (6): 1697-1701.DOI: 10.11772/j.issn.1001-9081.2017.06.1697

基于用户身份特征的多标签分类算法

郑晓雪^1,2, 张大方^1,2, 刁祖龙^1,2

1. 湖南大学信息科学与工程学院, 长沙 410082;
2. 湖南大学可信系统与网络实验室, 长沙 410082

收稿日期:2016-11-14 修回日期:2017-01-16 出版日期:2017-06-10 发布日期:2017-06-14
通讯作者: 郑晓雪
作者简介:郑晓雪(1990-),女,吉林松原人,硕士研究生,主要研究方向:数据挖掘、机器学习;张大方(1959-),男,上海人,教授,博士,CCF会员,主要研究方向:可信系统与网络、软件测试、下一代互联网;刁祖龙(1988-),男,湖南株洲人,博士研究生,主要研究方向:大数据、数据挖掘、机器学习。

Multi-label classification algorithm based on user identity

ZHENG Xiaoxue^1,2, ZHANG Dafang^1,2, DIAO Zulong^1,2

1. College of Computer Science and Electronic Engineering, Hunan University, Changsha Hunan 410082, China;
2. Laboratory of Dependable Systems and Network, Hunan University, Changsha Hunan 410082, China

Received:2016-11-14 Revised:2017-01-16 Online:2017-06-10 Published:2017-06-14

摘要/Abstract

摘要： 目前对于智慧校园中的家校沟通，缺乏一种衡量和参考的方法。针对智慧校园中特有的聊天特点即存在明显的身份特征，提出了一种基于用户身份特征的多标签分类算法——Adaboost.ML。首先，新增加了启发式规则；然后，引入Adaboost.MH算法，同时摒弃了把数据集进行分片的概念；最后，直接利用单条数据作为分析的焦点，减少了由于时间片边缘带来的误差和推断时间，综合决策出聊天用户之间的关联关系。实验结果表明，与基于规则的启发式方法相比，所提算法在智慧校园数据集上的误报率、漏报率分别降低了53%、66%，同时在微信数据集上也具有良好的分类效果。该算法已应用到智慧校园项目中，能够迅速并准确地了解到家校沟通的情况。

关键词: 社会网络, 智慧校园, 启发式规则, 多标签判断, 集成学习

Abstract: At present there lacks a way to measure home-school communication in a smart campus. Concerning the obvious identity characteristics when chatting in a smart campus, a new multi-label classification algorithm named Adaboost.ML (Multiclass, multi-label version of Adaboost based on user identity) was proposed. Firstly, the heuristic rule was added for the proposed algorithm. Then, the Adaboost.MH (Multiclass,multi-label version of Adaboost based on Hamming loss) algorithm was introduced, and the concept of dataset sharding was discarded. Finally, the single data was used as the focus of analysis, which reduced the inference time and the error caused by the edge of the time slice. The comprehensive decision-making about the relationship between the chat users was made out. The experimental results show that, compared with the heuristic algorithm based on rules, the false positive rate of the proposed algorithm is decreased by 53% while its false negative rate is reduced by 66% on the dataset of smart campus. The proposed algorithm also has good classification results on the dataset of WeChat. At present, the proposed algorithm has been applied to the smart campus project, and it can get home-school communication quickly and accurately.

Key words: social network, smart campus, heuristic rule, multi-label judgment, ensemble learning

中图分类号:

郑晓雪, 张大方, 刁祖龙. 基于用户身份特征的多标签分类算法[J]. 计算机应用, 2017, 37(6): 1697-1701.

ZHENG Xiaoxue, ZHANG Dafang, DIAO Zulong. Multi-label classification algorithm based on user identity[J]. Journal of Computer Applications, 2017, 37(6): 1697-1701.

参考文献

[1] HUANG R H, ZHANG J B, HU Y B,et al. Smart campus:the developing trends of digital campus[J]. Open Education Research, 2012, 18(4):12-17.
[2] KEERTHI S S, SHEVADE S K, BHATTACHARYYA C, et al. Improvements to Platt's SMO algorithm for SVM classifier design[J]. Neural Computation, 2001, 13(3):637-649.
[3] TSOUMAKAS G, KATAKIS I, TANIAR D. Multi-label classification:an overview[J]. International Journal of Data Warehousing and Mining, 2007, 3(3):1-13.
[4] SCHAPIRE R E, SINGER Y. Improved boosting algorithms using confidence-rated predictions[J]. Machine Learning, 1999, 37(3):297-336
[5] SCHAPIRE R E,SINGER Y. BoosTexter:a boosting based system for text categorization[J].Machine Learning, 2000, 39(2/3):135-168
[6] ELISSEEFF A, WESTON J. A kernel method for multi-labelled classification[C]//NIPS 2001:Proceedings of the Annual Conference on Neural Information Processing Systems. Cambridge, MA:MIT Press, 2001:681-687.
[7] 万书鹏.基于两类和三类支持向量机的快速多标签分类算法[D].南京:南京师范大学,2008:4-7.(WAN S P. A fast multi-label classification algorithm based on binary and triple class support vector machines[D]. Nanjing:Nanjing Normal University,2008:4-7.)
[8] FOX C R, CLEMEN R T. Subjective probability assessment in decision analysis:partition dependence and bias toward the ignorance prior[J]. Management Science, 2005, 51(9):1417-1432.
[9] SPEIRS-BRIDGE A, FIDLER F, MCBRIDE M, et al. Reducing overconfidence in the internal judgments of experts[J]. Risk Analysis, 2010, 30(3):512-523.
[10] TROHIDIS K, TSOUMARKS G,KALLIRIS G, et al. Multi-label classification of music into emotions[C]//ISMIR 2008:Proceedings of the 9th International Conference on Music Information Retrieval. Philadelphia:[s.n.], 2008:325-330.
[11] MUTTON P. Inferring and visualizing social networks on Internet relay chat[C]//IV'04:Proceedings of the 8th International Conference on Information Visualization. Washington, DC:IEEE Computer Society, 2004:35-43.
[12] 张卫,曹先彬,尹洪章.基于多特征融合的聊天室社会网络挖掘方法[J].中国科学技术大学学报,2009,39(5):540-546.(ZHANG W, CAO X B, YIN H Z. Chat room social network mining based on multi-features fusion[J]. Journal of University of Science and Technology of China, 2009, 39(5):540-546.)
[13] 张丹普,付忠良,王莉莉,等.基于浮动阈值分类器组合的多标签分类算法[J].计算机应用,2015,35(1):147-151.(ZHANG D P, FU Z L, WANG L L, et al. Multi-label classification algorithm based on floating threshold classifiers combination[J]. Journal of Computer Applications, 2015, 35(1):147-151.)

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基于用户身份特征的多标签分类算法

Multi-label classification algorithm based on user identity

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