Background:
With the rapid development of the Internet, the number of web spam has
increased dramatically in recent years, which has wasted search engine storage and computing power
on a massive scale. To identify the web spam effectively, the content features, link features, hidden
features and quality features of web page are integrated to establish the corresponding web spam
identification index system. However, the index system is highly correlation dimension.
Methods:
An improved method of autoencoder named stacked autoencoder neural network (SAE) is
used to realize the reduction of the web spam identification index system.
Results:
The experiment results show that our method could reduce effectively the index of web
spam and significantly improves the recognition rate in the following work.
Conclusion:
An autoencoder based web spam indexes reduction method is proposed in this paper.
The experimental results show that it greatly reduces the temporal and spatial complexity of the future
web spam detection model.
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification