计算机科学与探索2017，Vol.11Issue(7)：1166-1174,9.DOI:10.3778/j.issn.1673-9418.1605005
稀疏混合图随机跳跃Web对象多标签半监督分类
Sparse Mixed Graph Random Jump Transition Policy for Web Object Multi-Label Classification
摘要
Abstract
In order to solve the problem of time consuming and insufficient for labeling data, which leads the low computational efficiency in multi-label classification of Web objects, this paper proposes a multi-label classification algorithm based on sparse mixed graph random jump transition strategy for Web object. Firstly, based on the con-struction of the Web object affinity graph and tag correlation, weight adaptive method is used to construct a hybrid graph of Web object label classification, which realizes the automatic annotation of semi-supervised form and solves the time consuming problem of manual annotation;Secondly, in order to solve the problem of mixed graph, the ran-dom jump transition strategy is used to get the probability distribution between the mixed graph and the prediction tag, which realizes the probability estimation of the class label of the unlabeled Web object and obtains the highesttop-k correlation score;Finally, through the test on UCI Web dataset and real big data, the results show that the Rand index of the proposed algorithm is better than the selected contrast algorithms, which verifies the effectiveness of the proposed algorithm.关键词
大数据/随机跳跃/Web对象/标签分类/自动标注Key words
big data/random jump/Web object/label classification/automatic marking分类
信息技术与安全科学引用本文复制引用
汪忠国,吴敏,谭芳芳..稀疏混合图随机跳跃Web对象多标签半监督分类[J].计算机科学与探索,2017,11(7):1166-1174,9.基金项目
The Natural Science Research Project of Education Department of Anhui Province under Grant No. KJ2016A075 (安徽省教育厅自然科学研究项目). （安徽省教育厅自然科学研究项目）
