Feature selection using linear classifier weights

Dunja Mladenić, Janez Brank, Marko Grobelnik, Natasa Milic-Frayling
2004 Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR '04  
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 from
more » ... inear SVMs yields better classification performance than other feature weighting methods when combined with the three explored learning algorithms. The results support the conjecture that it is the sophistication of the feature weighting method rather than its apparent compatibility with the learning algorithm that improves classification performance.
doi:10.1145/1008992.1009034 dblp:conf/sigir/MladenicBGM04 fatcat:ah2ixcaljfda5fdc2sp2cqne5y