A New KNN Categorization Algorithm for Harmful Information Filtering
The prediction result of classifier is biased towards the class with more samples, when the harmful text information is filtered. This is because that the samples that including the harmful information were difficult to gain. Construct virtual samples is an effective means to solve the problem of pattern recognition in the small sample, using the upsampling method to construct virtual samples in the data layer, the traditional KNN algorithm has been improved: a small sample set is divided into clusters by using the K-means clustering, the virtual samples are generated and verified the validity in the cluster. The experimental results show that this method can construct the virtual samples which are similar to the real sample characteristics, and improved the classification effect of KNN algorithm.
component Small sample pattern recognition Virtual sample Harmful information filtering Network information security
Juan DU Zhi an Yi
Software College, Northeast Petroleum University Da qing, China
国际会议
杭州
英文
489-492
2012-10-28（万方平台首次上网日期，不代表论文的发表时间）