A ML-KNN method based on attribute weighting has been proposed.To be specific,we first identify samples from the non-positive regions of decision classes by means of the variable precision neighborhood rough set model with respect to each label and construct the heterogeneous sample pairs.Then,the significance of different attributes for classification is evaluated based on their discernibility for the heterogeneous sample pairs.Finally,the weighted distances between samples are calculated in order to ob-tain the nearest neighbor distributions of samples.At the same time,based on the principle of maximizing the posterior probability,the multi-label classification is implemented.Further,the experimental results on ten public multi-label data sets verify the effective-ness of the proposed method.
multi-label classificationattribute significanceneighborhood rough setuncertainty of classificationheterogeneous sample pair
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