Designing a web spam classifier based on feature fusion in the Layered Multi-population Genetic Programming framework

A. Keyhanipour, B. Moshiri
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引用次数: 16

Abstract

Nowadays, Web spam pages are a critical challenge for Web retrieval systems which have drastic influence on the performance of such systems. Although these systems try to combat the impact of spam pages on their final results list, spammers increasingly use more sophisticated techniques to increase the number of views for their intended pages in order to have more commercial success. This paper employs the recently proposed Layered Multi-population Genetic Programming model for Web spam detection task as well application of correlation coefficient analysis for feature space reduction. Based on our tentative results, the designed classifier, which is based on a combination of easy to compute features, has a very reasonable performance in comparison with similar methods.
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分层多种群遗传规划框架下基于特征融合的垃圾邮件分类器设计
目前,Web垃圾页面是Web检索系统面临的一个重要挑战,它对Web检索系统的性能有很大的影响。虽然这些系统试图消除垃圾页面对最终结果列表的影响,但垃圾邮件发送者越来越多地使用更复杂的技术来增加他们预期页面的浏览量,以获得更多的商业成功。本文采用最近提出的分层多种群遗传规划模型进行Web垃圾邮件检测任务,并应用相关系数分析进行特征空间约简。根据我们的初步结果,所设计的分类器基于易于计算的特征组合,与同类方法相比具有非常合理的性能。
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