LZ complexity similarity based spam detection
Hongbo He · Computer Engineering and Applications Journal · 2007
A spam detection method is proposed based on the LZ complexity similarity of symbolic sequences and K nearest neighbor rule.Compared to approaches based on vector space model,the calculation of the LZ complexity similarity between email documents requires neither text preprocessing nor feature extraction.The lazy learning characteristic of K nearest neighbor rule facilitates the application environment that the spam sample set needs to be adjusted dynamically.The proposed method has been tested on the Ling-Spam dataset using a 10-Fold cross validation.The total detection effect is better than the results of some contrast methods based on vector space model.