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Feature selection is used for finding a feature subset that has the most discriminative information from the original feature set. Large number of features often includes many garbage features. We propose a novel feature selection method on the basis of the estimation of Bayes discrimination boundary. The experimental results on heart single proton emission computed tomography (SPECT) data shows the fundamental effectiveness of the proposed method compared to the conventional forward feature selection methods.
Date of Conference: 16-18 May 2008