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Fuzzy Rough Decision Trees for Multi-label Classification

  • Xiaoxue Wang
  • Shuang An
  • Hong Shi
  • Qinghua HuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)

Abstract

Multi-label classification exists widely in medical analysis or image annotation. Although there are some algorithms to train models for multi-label classification, few of them are able to extract comprehensible rules. In this paper, we propose a multi-label decision tree algorithm based on fuzzy rough sets, named ML-FRDT. This method can tackle with symbolic, continuous and fuzzy data. We conduct experiments on two multi-label datasets. And the experiment results show that ML-FRDT achieves good performance than some well-established multi-label classification algorithms.

Keywords

Multi-label learning Fuzzy rough sets Decision tree 

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Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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