Spam Filtering Based on Kernel Partial Least Squares Classification
Dai Yu-juan · Zhongwen xinxi xuebao · 2009
The spam is one of the most serious problems to be resolved in the Internet.Recently,several spam filtering technologies have been proposed and applied to spam filtering,such as the Partial Least Squares(PLS) method.The PLS method can deal with the sparse data,the high dimensionalities and the multi-colinearity issues existing in the e-mail dataset.However,the latent content relationships among the e-mail data are,more often than not,nonlinear.This paper introduces the kernel function over PLS method to capture such non-linearity.Compared with PLSR method,the proposed KPLS model is proved with superior efficiency in the experiments on the Enron-Spam dataset.