Feature ranking and selection used in a machine learning framework for predicting uniformity of printed pages

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