摘要
针对传统非法文本特征自适应提取中,普遍存在着提取查全率较低、完成时间较长、成本消耗较大等问题。对此问题,提出一种基于支持向量机回归的非法文本特征自适应提取方法。对互联网文本特征信息进行分析,通过灰度局部的显著非法文本特征变化和显著非法文本特征多方向边缘强度,定位出显著非法文本特征区域,引入支持向量回归模型对非法文本特征区域和合法文本特征区域进行精确分离,消除合法文本边界,提取出互联网中非法文本特征。实验结果表明,所提出方法提取查全率较高、完成时间较短、成本消耗较低。
In the traditional adaptive extraction of illegal text feature,the extraction recall rate is low,the completion time is long and the cost is high.Therefore,a method for adaptively extracting illegal text features based on support vector machine regression was proposed.At first,the Internet text feature information was analyzed.Then,the significant illegal text feature region was located through the grayscale local significant change of illegal text feature and the multi-direction edge intensity of significant illegal text feature.In addition,the support vector regression model was introduced to precisely separate the region with illegal text feature from the region with legal text feature,so as to eliminate the legal text boundary.Finally,the illegal text feature was extracted from the Internet.Simulation results show that the proposed method has higher recall ratio,shorter completion time and lower cost.
作者
杨肖楠
花季伟
YANG Xiao-nan;HUA Ji-wei(College Of Computer and Information Engineering,Tianjin Normal University 300387,China)
出处
《计算机仿真》
北大核心
2019年第6期434-437,共4页
Computer Simulation
关键词
互联网
非法文本特征
自适应提取
支持向量
Internet
Illegal text feature
Adaptive extraction
Support vector
