基于偏最小二乘特征抽取的垃圾邮件过滤

随着垃圾邮件逐渐成为网络用户的一大困扰,垃圾邮件过滤技术的研究显得越来越重要。针对电子邮件存在数据极度稀疏性、高特征维数和多重相关性等特点,本文提出了一种基于偏最小二乘原理的特征抽取方法,可以通过对原始特征进行线性组合抽取出既可反映邮件内容又可反映邮件类型的潜在语义特征,并可解决多重相关性问题。在Enron-Spam邮件数据集上的实验结果表明,同χ²特征选择方法相比,该方法在较低维数上可以获取良好的邮件过滤性能。

Spam Filtering Based on PLS Feature Extraction

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 χ² statistics feature selection.