Abstract
Text Categorization is the process of assigning documents to a set of previously fixed categories. A lot of research is going on with the goal of automating this time-consuming task. Several different algorithms have been applied, and Support Vector Machines have shown very good results. In this paper we propose a new family of measures taken from the Machine Learning environment to apply them to feature reduction task. The experiments are performed on two different corpus (Reuters and Ohsumed). The results show that the new family of measures performs better than the traditional Information Theory measures.
The research reported in this paper has been supported in part under MCyT and Feder grant TIC2001-3579 and FICYT grant BP01-114.
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Montañés, E., Fernández, J., Díaz, I., Combarro, E.F., Ranilla, J. (2003). Measures of Rule Quality for Feature Selection in Text Categorization. In: R. Berthold, M., Lenz, HJ., Bradley, E., Kruse, R., Borgelt, C. (eds) Advances in Intelligent Data Analysis V. IDA 2003. Lecture Notes in Computer Science, vol 2810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45231-7_54
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DOI: https://doi.org/10.1007/978-3-540-45231-7_54
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