A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection |
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Authors: | Xiao-Yong Lu Mu-Sheng Chen Jheng-Long Wu Pei-Chan Chang Meng-Hui Chen |
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Affiliation: | 1.School of Software Engineering,Nanchang University,Nanchang,China;2.School of Information Engineering,Nanchang University,Nanchang,China;3.Software School,Nanchang University,Nanchang,China;4.Institute of Information Science,Academia Sinica,Taipei,Taiwan;5.Information Management and Innovation Center for Big Data and Digital Convergence,Yuan Ze University,Taoyuan,Taiwan |
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Abstract: | 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. |
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