Designing a web spam classifier based on feature fusion in the layered multi-population genetic programming framework

• KEYHANIPOUR, Amir Hosein ^[1] ; MOSHIRI, Behzad ^[1]
  1. [1] University of Tehran

     University of Tehran

     Irán

     
• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 2, Nº. 6, 2013, págs. 15-27
• Idioma: inglés
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• Resumen

  • 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.