A Multi-Label Classifier Chain Algorithm Based on PageRank and Mutual Information
Classifier chains are a sort of multi-label classification algorithm.For the classifier chains algorithm,finding the appropriate label order is the key to improving the classification accuracy.In single-order mode,im-proper label order seriously affected the classification performance,while adopting random multiple-order mode brought the problem of increasing algorithm complexity.To address the above issues,a multi-label classifier chain algorithm based on PageRank and mutual information is proposed.First,the similarities between labels and web pages are explored,analogizing the similarity between labels to the links between web pages,and then con-sidering global relevance by using mutual information to measure the correlation between labels.Finally,based on the correlation information,the idea of PageRank to measure the importance of web pages is used to rank labels and form a classifier chain.Experiments on ten common multi-label data sets from different fields show that this method can find the appropriate label order for the classifier chains algorithm,improving the classification accura-cy and reducing the computational cost.
multi-label classificationclassifier chainsPageRanklabel correlationmutual information
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