一种自反馈垃圾信息综合过滤方法

A self-feedback synthesis method for spam filtering

提出了一种自反馈垃圾信息综合过滤方法.通过构建日志分析模块,在人为参与尽可能少的情况下,根据过滤到的垃圾信息通过自我分析、自我决策、自我优化来实现信息过滤规则的自反馈更新.试验证明该方法克服了传统海量信息过滤中人工参与度高、工作量大、效率和准确率与人的操作高度相关的缺点,大大提高了信息过滤速度和准确率,实现了信息过滤的自动化.

A self-feedback based spare filtering method has been developed. In the construction of the log analysis module, the filtering system was implemented in a way ＇that permited self-feedback when updating filtering rules. Self-analysis, self-decision, and self-optimization were all incorporated. In this way minimal human intervention was required. In traditional massive information filtering, human involvement was very high, leaving filtering accuracy and efficiency highly dependent on the skills of the human operator. Experiments proved that this method overcomes these shortcomings, greatly enhancing the speed and accuracy of information filtering and effectively automating information filtering.