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Volume: 29 | Article ID: art00024
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Feature ranking and selection used in a machine learning framework for predicting uniformity of printed pages
  DOI :  10.2352/ISSN.2470-1173.2017.12.IQSP-238  Published OnlineJanuary 2017
Abstract

In our previous work [1,2], we presented a block-based technique to analyze printed page uniformity both visually and metrically. In this paper, we introduce a new sets of tools for feature ranking and selection. The features learned from the models are then employed in a Support Vector Machine (SVM) framework to classify the pages into one of the two categories of acceptable and unacceptable quality. We utilize three methods in feature ranking including F-score, Linear-SVM weight, and Forward Search. The first two methods are filter methods while the last is categorized as a wrapper approach. We use the result from the wrapper method and information from the filter methods as confidence scores in our feature selection framework

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Minh Q. Nguyen, Jan P. Allebach, "Feature ranking and selection used in a machine learning framework for predicting uniformity of printed pagesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XIV,  2017,  pp 166 - 173,  https://doi.org/10.2352/ISSN.2470-1173.2017.12.IQSP-238

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Copyright © Society for Imaging Science and Technology 2017
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Electronic Imaging
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Society for Imaging Science and Technology