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
Annals of Data Science – Springer Journals
Published: Jun 6, 2018
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