Web Spam Detection Using Multiple Kernels in Twin Support Vector Machine

TL;DR

This paper enhances web spam detection accuracy by integrating multiple nonlinear kernels into Twin Support Vector Machine, demonstrating improved performance on standard datasets.

Contribution

Introduces a novel kernelized Twin SVM approach with dual kernels for each class, improving web spam detection accuracy over traditional methods.

Findings

Effective in identifying spam pages with high accuracy

Outperforms existing SVM-based spam detection methods

Validated on UK-2007 and UK-2006 datasets

Abstract

Search engines are the most important tools for web data acquisition. Web pages are crawled and indexed by search Engines. Users typically locate useful web pages by querying a search engine. One of the challenges in search engines administration is spam pages which waste search engine resources. These pages by deception of search engine ranking algorithms try to be showed in the first page of results. There are many approaches to web spam pages detection such as measurement of HTML code style similarity, pages linguistic pattern analysis and machine learning algorithm on page content features. One of the famous algorithms has been used in machine learning approach is Support Vector Machine (SVM) classifier. Recently basic structure of SVM has been changed by new extensions to increase robustness and classification accuracy. In this paper we improved accuracy of web spam detection by using two nonlinear kernels into Twin SVM (TSVM) as an improved extension of SVM. The classifier ability to data separation has been increased by using two separated kernels for each class of data. Effectiveness of new proposed method has been experimented with two publicly used spam datasets called UK-2007 and UK-2006. Results show the effectiveness of proposed kernelized version of TSVM in web spam page detection.