A fuzzy Dempster-Shafer classifier for detecting Web spams

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.

Automated summary

The paper presents a method for identifying web spams using fuzzy evidence and the Dempster-Shafer theory, improving classification accuracy. By combining fuzzy reasoning and DS theory, conflicts among evidence are reduced, providing a more reliable spam detection solution.