Computer Science ›› 2014, Vol. 41 ›› Issue (11): 260-264.doi: 10.11896/j.issn.1002-137X.2014.11.050

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Active Learning for Multi-label Classification on Graphs

LI Yuan-hang,LIU Bo and TANG Qiao   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Although active learning has been extensively used in study in graph data,little research has been done on active learning on multi-label classification with graph data.We proposed a novel approach for multi-label classification with graph data by using an active learning based on error bound minimization.We first obtained a series of equations by using multi-label classification and learning with local and global consistency (LLGC),so as to make the equation apply to minimize the transductive rademacher complexity and minimize the generalization error bound.By using the approach,we obtained the most informative sample data from graph data.Experiments show that our method can obtain high performance for multi-label classification.

Key words: Data on graph,Active learning,Complexity,Minimization

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