Bayesian Based Type Discrimination of Web Events
Bayesian Based Type Discrimination of Web Events
复制标题
基于贝叶斯的 Web 事件类型判别
DOI：
--
复制
发表时间：
2015-11
影响因子：
0.8
通讯作者：
Huimin Liu
中科院分区：
文献类型：
--
作者：
Qichen Ma;Xiangfeng Luo;Junyu Xuan;Huimin Liu
每天都有大量的网络事件出现在网络上，吸引着人们的注意力，区分这些网络事件的不同类型在实践中具有重要的意义。例如，政府部门应该更多地关注特殊的网络突发事件，以挽救生命和损失，新闻网站应该利用有限的资源提高点击率。然而，如何有效地区分网络事件的类型仍然是一个挑战性的问题，由于在这个问题上付出的努力很少在社区。在本文中，我们对此问题进行了深入的考虑，然后提出了一个创新的贝叶斯模型来区分不同类型的Web事件。具体而言，所有的Web事件，首先假定为三种类型，其形式化的定义，考虑到他们的属性。为了充分描述和区分三类Web事件，然后从Web事件的数量和内容中提取一组专门设计的特征。最后，基于设计的特征，提出了一种贝叶斯模型。实验结果表明，该模型能够区分Web事件的类型，并与其他国家的最先进的分类器的比较也表明了该模型的效率。
There are a large number of web events emerging on the web and attracting people's attention every day, and it is of great interest and significance to distinguish the different types of these web events in practice. For example, the distinguished emergent web events should be paid more attentions by the departments of the government to save lives and damages or by news websites to increase their hit-rates using limited resources. However, how to efficiently distinguish the types of web events remains a challenge issue due to the seldom efforts paid to this issue in the community. In this paper, we conduct a thorough consideration on this problem and then propose an innovative Bayesian-based model to distinguish the different types of web events. To be specific, all web events are firstly assumed within three types whose formal definitions are given by considering their properties. Aiming to sufficiently describe and distinguish three types web events, a set of specially designed features are then extracted from the volume and the content of web events. Finally, a Bayesian-based model is proposed based on the designed features. The experimental results demonstrate the capability of the proposed model to distinguish types of web events, and the comparisons with other state-of-the-art classifiers also show the efficiency of the proposed model.
登录
查看更多内容
DOI：
--
发表时间：
2014-11
期刊：
J. Web Eng.
影响因子：
--
作者：
Byung-Won On;Muhammad Omar;G. Choi;Junbeom Kwon
通讯作者：
Byung-Won On;Muhammad Omar;G. Choi;Junbeom Kwon
DOI：
10.1109/cvpr.2005.16
发表时间：
2005-06
期刊：
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
影响因子：
--
作者：
Li Fei-Fei-Li-Fei-Fei-48004138;P. Perona
通讯作者：
Li Fei-Fei-Li-Fei-Fei-48004138;P. Perona
DOI：
10.1007/978-3-540-25952-7_20
发表时间：
2004-06
期刊：
--
影响因子：
--
作者：
Jason Bengel;Susan Gauch;Eera Mittur;Rajan Vijayaraghavan
通讯作者：
Jason Bengel;Susan Gauch;Eera Mittur;Rajan Vijayaraghavan
影响因子：
2.5
作者：
D. Zelterman
通讯作者：
D. Zelterman
影响因子：
3.2
作者：
Qing Li;Rynson W. H. Lau;B. Wah;H. Ashman;E. Leung;Frederick W. B. Li;V. Lee
通讯作者：
Qing Li;Rynson W. H. Lau;B. Wah;H. Ashman;E. Leung;Frederick W. B. Li;V. Lee