Web spam classification method based on deep belief networks

With the development of the Internet, the number of web spam increases gradually, which has seriously affected the user experience of search engines. To improve the classification performance of web spam, the deep belief networks (DBN) is used for the first time, and it is effectively combined with the Synthetic Minority Over-Sampling Technique (SMOTE) and De-Noising Auto-Encoder (DAE) algorithm after the multi-aspect research and consideration. After multiple sets of experiments on WEBSPAM-UK2007 dataset, the results show that the classification method proposed in this paper improves the classification performance to a certain extent, which provides a good direction for the future classification of web spam.