Graph Representation and Semi-clustering Approach for Label Space Reduction in Multi-label Classification of Documents

An increasing number of large online text repositories require effective techniques of document classification. In many cases, more than one class label should be assigned to documents. When the number of labels is big, it is difficult to obtain required multi-label classification accuracy. Efficient label space dimension reduction may significantly improve classification performance. In the paper, we consider applying graph-based semi-clustering algorithm, where documents are represented by vertices with edge weights calculated according to the similarity of associated texts. Semi-clusters are used for finding patterns of labels that occur together. Such approach enables reducing label dimensionality. The performance of the method is examined by experiments conducted on real medical documents. The assessment of classification results, in terms of Classification Accuracy, F-Measure and Hamming Loss, obtained for the most popular multi-label classifiers: Binary Relevance, Classifier Chains and Label Powerset showed good potential of the proposed methodology.