Abstract
The traditional feature selection algorithms are limited to single-label data.Concerning this problem,multi-label ReliefF algorithm was proposed for multi-label feature selection.For multi-label data,based on label co-occurrence,this algorithm assumed the label contribution value was equal.Combined with three new methods calculating the label contribution,the updating formula of feature weights was improved.Finally a distinguishable feature subset was selected from original features.Classification experiments demonstrate that,with the same number of features,classification accuracy of the proposed algorithm is obviously higher than the traditional approaches.
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