Abstract
Feature selection serves for both reduction of the total amount of available data (removing of valueless data) and improvement of the whole behavior of a given induction algorithm (removing data that cause deterioration of the results). A method of proper selection of features for an inductive algorithm is discussed. The main idea consists in proper descending ordering of features according to a measure of new information contributing to previous valuable set of features. The measure is based on comparing of statistical distributions of individual features including mutual correlation. A mathematical theory of the approach is described. Results of the method applied to real-life data are shown.
The work was supported by Ministry of Education of the Czech Rep. under project LN00B096.
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Jirina, M., Jirina, M. (2005). Feature Selection by Reordering. In: Vojtáš, P., Bieliková, M., Charron-Bost, B., Sýkora, O. (eds) SOFSEM 2005: Theory and Practice of Computer Science. SOFSEM 2005. Lecture Notes in Computer Science, vol 3381. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30577-4_45
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DOI: https://doi.org/10.1007/978-3-540-30577-4_45
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