Multi-label Classification without the Multi-label Cost

  title={Multi-label Classification without the Multi-label Cost},
  author={Xiatian Zhang and Quan Yuan and Shiwan Zhao and Wei Fan and Wentao Zheng and Zhong Wang},
Multi-label classification, or the same example can belong to more than one class label, happens in many applications. To name a few, image and video annotation, functional genomics, social network annotation and text categorization are some typical applications. Existing methods have limited performance in both efficiency and accuracy. In this paper, we propose an extension over decision tree ensembles that can handle both challenges. We formally analyze the learning risk of Random Decision… CONTINUE READING
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