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

• Published in: Pattern analysis and applications : PAA Vol. 21; no. 3; pp. 741 - 754
• Main Authors: Lu, Xiao-Yong, Chen, Mu-Sheng, Wu, Jheng-Long, Chang, Pei-Chan, Chen, Meng-Hui
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2018; Springer Nature B.V
• Subjects: Algorithms; Classifiers; Computer Science; Datasets; Decision trees; Pattern Recognition; Sampling; Search engines; Spamming; Theoretical Advances; Feature selection; Web spam detection; Ensemble learning; Decision trees; Clonal selection algorithm
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-017-0602-2

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.