Spam Filtering Based on PLS Feature Extraction
Guobin Huang · Zhongwen xinxi xuebao · 2008
Along with the coming of network times,the research of spam filtering technology has been imperative under the situation.However, some specialties of mail dataset such as the data sparseness,high dimensionalities and multi-collinearity in mail content make great difference between spam filtering work and text classification work.In this paper,a Partial Least Squares(PLS) feature extraction method on spam filtering is proposed,which could extract latent semantic components that can capture the content information and class information,and could copy with the multi-collinearity.The experiments on Enron-Spam database show that our method can get very good performance in spam filtering compared with χ~2 statistics feature selection.