The Web spam identification problem can be modeled as an instance of the conventional classification problem. Web spams aim at deceiving web crawlers by advertising certain Web pages through elevation of their page rankings superficially than their actual weights. Web spams are intended to produce fraudulent results of web search queries and degenerate the client’s experience by directing users to fake Web pages. We present a fuzzy evidence-based methodology for identifying Web spams by which the spamicity of web hosts is formulated as a reasoning problem in the presence of uncertainty. However, any classification task intrinsically suffers from incomplete or vague evidence and ambiguity in the class assignment based on evidence. In this work, we combine fuzzy reasoning as the decision maker for selecting the most suitable evidence in a multi-source Dempster–Shafer (DS) based classification algorithm. The introduced approach has the benefit of providing more reliable solution to detect spams without any prior information. The evidence theory offers flexible support that takes into account the multi-dimensional nature of implementation decisions. The experimental results show that the fuzzy reasoning in combination with DS theory, reduces the conflicts among evidence leading to enhanced classification results. The aim of this paper is to describe the potential of fuzzy reasoning and the Dempster–Shafer Theory (DST) as a decision model for the web spams classification problem.

可以将Web垃圾邮件识别问题建模为常规分类问题的一个实例。网络垃圾邮件旨在通过表面上超出其实际权重的页面排名广告来宣传某些网页，从而欺骗网络爬虫。Web垃圾邮件旨在产生欺诈性的Web搜索查询结果，并通过引导用户访问伪造的Web页面来破坏客户的体验。我们提出了一个模糊的证据为基础的方法，用于识别网络垃圾邮件，通过该spamicity在存在不确定性的情况下，Web主机数量被制定为推理问题。但是，任何分类任务本质上都会遭受不完整或模糊的证据以及基于证据的班级分配的歧义。在这项工作中，我们结合模糊推理作为决策者，在基于多源Dempster–Shafer（DS）的分类算法中选择最合适的证据。引入的方法的好处是提供了更可靠的解决方案，无需任何先验信息即可检测垃圾邮件。证据理论提供了灵活的支持，考虑了执行决策的多维性质。实验结果表明，模糊推理与DS理论相结合，减少了证据之间的冲突，导致分类结果的提高。