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Machine learning based heterogeneous web advertisements detection using a diverse feature set - 2018

Machine Learning Based Heterogeneous Web Advertisements Detection Using A Diverse Feature Set

Research Paper on Machine Learning Based Heterogeneous Web Advertisements Detection Using A Diverse Feature Set

Research Area:  Machine Learning

Abstract:

Advertisement identification and filtering in web pages gain significance due to various factors such as accessibility, security, privacy, and obtrusiveness. Current practices in this direction involve maintaining URL-based regular expressions called filter lists. Each URL obtained on a web page is matched against this filter list. While effectual, this procedure lacks scalability as it demands regular continuance of the filter list. To counter these limitations, we devise a machine learning based advertisement detection system using a diverse feature set which can distinguish advertisement blocks from non-advertisement blocks. The method can act as a base to provide various accessibility-related features like smooth browsing and text summarization for persons with visual impairments, cognitive impairments, and photosensitive epilepsy. The results from a classifier trained on the proposed feature set achieve 98.6% accuracy in identifying advertisements.

Keywords:  
Machine Learning
Web Advertisements Detection
Diverse Feature Set
Deep Learning

Author(s) Name:  AbShaqoor Nengroo and K.S.Kuppusamy

Journal name:  Future Generation Computer Systems

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.future.2018.06.028

Volume Information:  Volume 89, December 2018, Pages 68-77