Comprehensive Literature Review on Machine Learning Structures for Web Spam Classification

AbstractVarious Web spam features and machine learning structures were constantly proposed to classify Web spam in recent years. The aim of this paper was to provide a comprehensive machine learning algorithms comparison within the Web spam detection community. Several machine learning algorithms and ensemble meta-algorithms as classifiers, area under receiver operating characteristic as performance evaluation and two public available datasets (WEBSPAM-UK2006 and WEBSPAM-UK2007) were experimented in this study. The results have shown that random forest with variations of AdaBoost had achieved 0.937 in WEBSPAM-UK2006 and 0.852 in WEBSPAM-UK2007