Abstract
Web spamming is nowadays 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 machine learning approach for spam detection by adopting the ant colony optimization algorithm. We first construct a directed graph corresponding to web hosts and their aggregated hyperlinks. Then, we train a classifier by employing ants to walk along paths in the graph. Each ant will start from an individual non-spam host and afterwards decides to follow a link to the next host with a probability based on both heuristic function and pheromone trail. Relying on the approximate isolation principle of a good set, we reward an ant that can discover a good path, i.e., a sequence of non-spam hosts, by charging energy for its longer walking. In contrast, if the ant instead discovers any spam, it will be penalized by decreasing its walking step. Finally, the classification rules are constructed by choosing common overlapping characteristic features of all non-spam hosts along the discovered paths. Experiments on WEBSPAM-UK2007 dataset show that our approach contributes to more accurately classify spam and non-spam hosts than several rule-based classification baselines.
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Manaskasemsak, B., Jiarpakdee, J., Rungsawang, A. (2014). Adaptive Learning Ant Colony Optimization for Web Spam Detection. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_48
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DOI: https://doi.org/10.1007/978-3-319-09153-2_48
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