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This paper explores feature scoring and selection based on weights from linear classification models. It investigates how these methods combine with various learning models. Our comparative analysis includes three learning algorithms: Naïve Bayes, Perceptron, and Support Vector Machines (SVM) in combination with three feature weighting methods: Odds Ratio, Information Gain, and weights from linear models, the linear SVM and Perceptron. Experiments show that feature selection using weights fromdoi:10.1145/1008992.1009034 dblp:conf/sigir/MladenicBGM04 fatcat:ah2ixcaljfda5fdc2sp2cqne5y