Kernel-Based Feature Fusion for Sensitive Information Filtering
Sensitive information filtering is the key technique to help people detect baneful information from the internet and insulate them for latter decisions.In this paper, based on the review about the characteristics and the hardness of sensitive information filtering task, we propose the idea of exploiting the combinational semantics of sensitive words to reinforce the filtering effect. Firstly, a new kernel, named as geometric-mean-ANOVA kernel, is introduced to generate features with specific combination degree. Further, multiple kernels across different combination degrees are fused together by composite kernel to produce the full feature space for the sensitive information filtering task. The evaluation in the real environment shows our kernel-based feature fusion methods exhibit superiority to those methods only using single kernel on both recall and precision.
features fusion kernel method combinational semantics sensitive information filtering
Wenbo Li Le Sun
Institute of Software Chinese Academy of Sciences 4# South Fourth Street, Zhong Guan Cun, Beijing
国际会议
The International Colloquium on Onformation Fusion 2007(2007年国际信息融合研讨会)
西安
英文
470-475
2007-08-22（万方平台首次上网日期，不代表论文的发表时间）