Multi-label Classification Using Rough Sets

  • Ying Yu
  • Duoqian Miao
  • Zhifei Zhang
  • Lei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8170)


In multi-label classification, each instance may be associated with multiple labels simultaneously which is different from the traditional single-label classification where an instance is only associated with a single label. In this paper, we propose two types of approaches to deal with multi-label classification problem based on rough sets. The first type of approach is to transform the multi-label problem into one or more single-label problems and then use the classical rough set model to make decisions. The second type of approach is to extend the classical rough set model in order to handle multi-label dataset directly, where the new model considers the correlations among labels. The effectiveness of multi-label rough set model is presented by a series of experiments completed for two multi-label datasets.


rough sets multi-label classification correlation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ying Yu
    • 1
    • 2
    • 3
  • Duoqian Miao
    • 1
    • 2
  • Zhifei Zhang
    • 1
    • 2
  • Lei Wang
    • 1
    • 2
  1. 1.Department of Computer Science and TechnologyTongji UniversityShanghaiP.R. China
  2. 2.Key Laboratory of Embedded System and Service Computing, Ministry of EducationTongji UniversityShanghaiP.R. China
  3. 3.Software SchoolJiangxi Agriculture UniversityP.R. China

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