<abstract xmlns="http://www.w3.org/1999/xhtml"><p> A rapid growth of online advertisements results in unsolicited bulk of data being downloaded during web surfing. To tackle this problem a fast mechanism detecting adverts is required. In this paper we present the usefulness of URL based web-pages classification in the process of online advertisements detection. Our experiments are performed on seven popular classifiers using the real-life dataset obtained by human agents browsing the internet. We introduce a general and fully automated framework that allows us to do a comprehensive analysis by performing simultaneously hundreds of experiments. This study results in solution with 0.987 accuracy and 0.822 F-measure.</p></abstract>

A rapid growth of online advertisements results in unsolicited bulk of data being downloaded during web surfing. To tackle this problem a fast mechanism detecting adverts is required. In this paper we present the usefulness of URL based web-pages classification in the process of online advertisements detection. Our experiments are performed on seven popular classifiers using the real-life dataset obtained by human agents browsing the internet. We introduce a general and fully automated framework that allows us to do a comprehensive analysis by performing simultaneously hundreds of experiments. This study results in solution with 0.987 accuracy and 0.822 F-measure.

An Automated Framework with Application to Study Url Based Online Advertisements Detection

Institute of Informatics, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland

T-Mobile Poland, Al. Jerozolimskie 181, 02-222 Warsaw, Poland

Journal of Applied Mathematics, Statistics and Informatics

A rapid growth of online advertisements results in unsolicited bulk of data being downloaded during web surfing. To tackle this problem a fast mechanism...

An Automated Framework with Application to Study Url Based Online Advertisements Detection

Publié en ligne: 24 août 2013

Pages: 47 - 60

DOI: https://doi.org/10.2478/jamsi-2013-0005

This content is open access.