Abstract: Web spam not only declines the quality of information retrieval,but also causes troubles to the security of Internet.This paper proposed a Bagging-based integration of SVM to detect Web spam.In preprocessing stage,a technique referring to K-means is introduced to solve the class-imbalance problem of dataset firstly,and then an optimal feature subset is culled by using CFS.Finally the optimal feature subset is discretized by the information entropy.In the stage of classifier training,several training datasets are obtained by Bagging and each training dataset is utilized to produce weak classifier respectively after SVM learning.In detection stage,test samples are voted by weak classifiers obtained before detemining their categories.Experimental results on the WEBSPAM-UK2006 reveal that the proposed method can achieve better results with less number of features.

Key words: Web spam,Integrated classifier,Feature selection,Information entropy,Weak classifier