Abstract The performance of existing methods for bookmark spam detection is decreased when there is less user profile information. An ensemble SVM approach integrated with confidence for detecting bookmark spam is proposed to solve this problem. The Bootstrap technology is firstly used to repeatedly sample the training data so as to get the subset of training samples for individual SVM. Then, sigmoid function is use to transform the standard output of SVM into a posterior probability which is used as the confidence of categories output. Finally, a method integrated with the confidence is proposed to aggregate the output of individual SVM, which is better than voting strategy. The experimental results show that the detection performance of the proposed approach outperforms the existing methods in the case of less user profile information.
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2011, Vol. 24 
