MULTI-LAYER FEATURES BASED PERSONALIZED SPAM FILTERING

摘要：

In this paper, we face a new challenge that the filter is expected to converge much faster, e.g. within 10 labeled SMSs or less. Topic model based dimension reduction can minimize the structural risk with limited training data. But dimension reduction will go against the completeness of feature space. It is very difficult to obtain the convergence rate and the completeness at the same time only by one kind of feature. This paper uses supervised dual-PLSA for Dimensionality Reduction and presents a multi-layer features model, which employs two layer features and adopts a novel method to combine them. Experiments show that multi-layer features model have the best performance.

关键词： Spam Filtering Personalized Filtering PLSA dual-PLSA Multi-layer features

作者: Weiran Xu Zhanyi Wang Dongxin Liu Jun Guo Rile Hu

作者单位: School of Information and Communication Engineering,Beijing University of Posts and Telecommunicatio Nokia Research Center(China), Beijing

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

会议地点: 北京

会议语种:英文

页码: 368-373

在线出版日期: 2009-11-06（万方平台首次上网日期，不代表论文的发表时间）