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

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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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Notes

  1. The participants and their experimental results can be found in the web spam challenge website (http://webspam.lip6.fr/wiki/pmwiki.php?n=Main.PhaseIResults).

  2. The participants and their experimental results can be found in the web spam challenge website (http://webspam.lip6.fr/wiki/pmwiki.php?n=Main.PhaseIIIResults).

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Authors and Affiliations

  1. School of Software Engineering, Nanchang University, Nanchang, 330047, China

    Xiao-Yong Lu, Pei-Chan Chang & Meng-Hui Chen

  2. School of Information Engineering, Nanchang University, Nanchang, 330031, China

    Mu-Sheng Chen

  3. Software School, Nanchang University, Nanchang, 330031, China

    Mu-Sheng Chen

  4. Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan

    Jheng-Long Wu

  5. Information Management and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, 32003, Taiwan

    Pei-Chan Chang

Authors
  1. Xiao-Yong Lu
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  2. Mu-Sheng Chen
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  3. Jheng-Long Wu
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  4. Pei-Chan Chang
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  5. Meng-Hui Chen
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Correspondence to Pei-Chan Chang.

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Lu, XY., Chen, MS., Wu, JL. et al. A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection. Pattern Anal Applic 21, 741–754 (2018). https://doi.org/10.1007/s10044-017-0602-2

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