Abstract
Mutual Information (MI) is a good selector of relevance between input and output feature and have been used as a measure for ranking features in several feature selection methods. Theses methods cannot estimate optimal feature subsets by themselves, but depend on user defined performance. In this paper, we propose estimation of optimal feature subsets by using rough sets to determine candidate feature subset which receives from MI feature selector. The experiment shows that we can correct nonlinear problems and problems in situation of two or more combined features are dominant features, maintain an improve classification accuracy.
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Foitong, S., Rojanavasu, P., Attachoo, B., Pinngern, O. (2009). Estimating Optimal Feature Subsets Using Mutual Information Feature Selector and Rough Sets. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_103
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DOI: https://doi.org/10.1007/978-3-642-01307-2_103
Publisher Name: Springer, Berlin, Heidelberg
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