@inproceedings{9b3b3ac274dd4d1fafa7377f4a5bd066,
title = "Feature selection using linear classifier weights: Interaction with classification models",
abstract = "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: Na{\"i}ve 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 linear 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.",
keywords = "Feature scoring, Feature selection, Information retrieval, Linear SVM, SVM normal, Text classification, Vector representation",
author = "Dunja Mladeni{\'c} and Janez Brank and Marko Grobelnik and Natasa Milic-Frayling",
year = "2004",
language = "English",
isbn = "1581138814",
series = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery (ACM)",
pages = "234--241",
booktitle = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",
address = "United States",
note = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ; Conference date: 25-07-2004 Through 29-07-2004",
}