SPAM DETECTING BASED ON FEATURE EXTRACTION ALGORITHM
BU Hua-lon · Journal of Chaohu College · 2014
As a typical text classification application problem, spam detecting is confused by the high dimensional data problems.In order to improve the efficiency and accuracy of spam detection, this paper proposes an intrusion detection algorithm based on PLS and SVM. The original spam data is projected through using the partial least squares algorithm to reduce the dimension of feature extraction and the genetic algorithm to find the best presented features, and the data is classified by the support vector machine. The Matlab simulation experiments show that our methods can effectively reduce the dimension of data and improve the accuracy of detecting.