Mining and Classifying Images from an Advertisement Image Remover

Mining and Classifying Images from an Advertisement Image Remover Ann. Data. Sci. https://doi.org/10.1007/s40745-018-0164-1 Mining and Classifying Images from an Advertisement Image Remover Graeme O’Meara Received: 13 July 2017 / Revised: 22 March 2018 / Accepted: 30 April 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract AdEater is an early browsing assistant that automatically removes adver- tisement images from internet pages. It works by generating rules from training data and implementing these rules when browsing the internet. Advertisement images on web pages are replaced by transparent images that display on the image the word “ad”, and where images are misclassified, non-advertisement images on a webpage will also be replaced by transparent images displaying “ad”. This paper critically examines the dataset derived from a trial of AdEater and tries to build a robust image classifier. We apply data mining techniques to uncover associations between features of advertise- ments and non-advertisements and try to predict whether the images are advertisements or non-advertisements based on three classification methods. We achieve classification accuracy of 96.5%, using k-fold cross validation to train and test the model. Keywords AdEater · Classification trees · Machine learning · Data mining · Artificial intelligence · Support vector machine · k-means clustering · Silhouette · Association http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Mining and Classifying Images from an Advertisement Image Remover

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Business and Management; Business and Management, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Computing Methodologies
ISSN
2198-5804
eISSN
2198-5812
D.O.I.
10.1007/s40745-018-0164-1
Publisher site
See Article on Publisher Site

Abstract

Ann. Data. Sci. https://doi.org/10.1007/s40745-018-0164-1 Mining and Classifying Images from an Advertisement Image Remover Graeme O’Meara Received: 13 July 2017 / Revised: 22 March 2018 / Accepted: 30 April 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract AdEater is an early browsing assistant that automatically removes adver- tisement images from internet pages. It works by generating rules from training data and implementing these rules when browsing the internet. Advertisement images on web pages are replaced by transparent images that display on the image the word “ad”, and where images are misclassified, non-advertisement images on a webpage will also be replaced by transparent images displaying “ad”. This paper critically examines the dataset derived from a trial of AdEater and tries to build a robust image classifier. We apply data mining techniques to uncover associations between features of advertise- ments and non-advertisements and try to predict whether the images are advertisements or non-advertisements based on three classification methods. We achieve classification accuracy of 96.5%, using k-fold cross validation to train and test the model. Keywords AdEater · Classification trees · Machine learning · Data mining · Artificial intelligence · Support vector machine · k-means clustering · Silhouette · Association

Journal

Annals of Data ScienceSpringer Journals

Published: Jun 6, 2018

References

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