A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

作者：Lu, Xiao-Yong; Chen, Mu-Sheng; Wu, Jheng-Long; Chang, Pei-Chan^*; Chen, Meng-Hui

来源：Pattern Analysis and Applications, 2018, 21(3): 741-754.

DOI：10.1007/s10044-017-0602-2

• Web spam detection
• Ensemble learning
• Clonal selection algorithm
• Feature selection
• Decision trees

Currently, web spamming is a serious problem for search engines. It not only degrades the quality of search results by intentionally boosting undesirable web pages to users, but also causes the search engine to waste a significant amount of computational and storage resources in manipulating useless information. In this paper, we present a novel ensemble classifier for web spam detection which combines the clonal selection algorithm for feature selection and under-sampling for data balancing. This web spam detection system is called USCS. The USCS ensemble classifiers can automatically sample and select sub-classifiers. First, the system will convert the imbalanced training dataset into several balanced datasets using the under-sampling method. Second, the system will automatically select several optimal feature subsets for each sub-classifier using a customized clonal selection algorithm. Third, the system will build several C4.5 decision tree sub-classifiers from these balanced datasets based on its specified features. Finally, these sub-classifiers will be used to construct an ensemble decision tree classifier which will be applied to classify the examples in the testing data. Experiments on WEBSPAM-UK2006 dataset on the web spam problem show that our proposed approach, the USCS ensemble web spam classifier, contributes significant classification performance compared to several baseline systems and state-of-the-art approaches.

• 出版日期2018-8
• 单位南昌大学; 中国科学院