Towards Web Spam Filtering using a Classifier based on the Minimum Description Length Principle

The steady growth and popularization of the Web has led spammers to develop techniques to circumvent search engines aiming good visibility to their web pages in search results. They are responsible for serious problems such as dissatisfaction, irritation, exposure to unpleasant or malicious content, and financial loss. Despite different machine learning approaches have been used to detect web spam, many of them suffer with the curse of dimensionality or require a very high computational cost impeding their employment in real scenarios. In this way, there is still a big effort to develop more advanced methods that at the same time are able to prevent overfitting and fast to learn. To fill this gap, we present the MDLClass, a classifier technique based on the minimum description length principle, applied to the context of web spam filtering. The proposed method is very efficient, lightweight, multi-class, and fast. We also evaluated a new approach to detect web spam that combines the predictions obtained by the classifiers using content-based, link-based, and transformed link-based features. In our experiments, we employed two real, public and large datasets: the WEBSPAM-UK2006 and the WEBSPAM-UK2007. The results indicate that the proposed MDLClass and ensemble of predictions using different types of features are promising in the task of web spam filtering.