BAO Ming, GUAN Lu-yang, LI Xiao-dong, et al. A Study on Optimum Classification Character Based on the Distributive Entropy of Euclidian Distance[J]. Acta Electronica Sinica, 2007, 35(3): 469-473.
BAO Ming, GUAN Lu-yang, LI Xiao-dong, et al. A Study on Optimum Classification Character Based on the Distributive Entropy of Euclidian Distance[J]. Acta Electronica Sinica, 2007, 35(3): 469-473.DOI：
An improved Kullback-Leiber distance is presented as a separable criterion for optimizing feature selection problems in pattern classification.A nonlinear parameter
Distributive Entropy of Euclidian Distance(DEED)
is introduced and based on which
the ratio of between-class DEED to within-class DEED (JRd) is defined as a criterion for the feature selection.DEED is a nonlinear measure for learning feature space
which gives the congregation and information measure of learning samples space.According to the result of Gaussian data experiments
it is concluded that the larger JRd be
the better separability of learning samples would be.
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