A method of spam filtering based on weighted support vector machines

The problem of content-based spam filtering on machine learning methods actually is a binary classification. SVMs can separate the data into two categories optimally so SVMs suit to spam filtering. With used into spam filtering, the standard support vector machine involves the minimization of the error function and the accuracy of the SVM is very high, but the degree of misclassification of legitimate emails is high. In order to solve that problem, this paper proposed a method of spam filtering based on weighted support vector machines. Experimental results show that the algorithm can enhance the filtering performance effectively.