Multi-label Attribute Evaluation Based on Fuzzy Rough Sets

  • Lingjun Zhang
  • Qinghua Hu
  • Yucan Zhou
  • Xiaoxue Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8536)

Abstract

In multi-label learning task, each sample may be assigned with one or more labels. Moreover multi-label classification tasks are often characterized by high-dimensional and inconsistent attributes. Fuzzy rough sets are an effective mathematic tool for dealing with inconsistency and attribute reduction. In this work, we discuss multi-label attribute reduction within the frame of fuzzy rough sets. We analyze the definitions of fuzzy lower approximation in multi-label classification and give several improvements of the traditional algorithms. Furthermore, the attribute dependency function is defined to evaluate condition attributes. A multi-label attribute reduction algorithm is constructed based on the dependency function. Numerical experiments show the effectiveness of the proposed technique.

Keywords

Multi-label learning attribute evaluation fuzzy rough set attribute dependency 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lingjun Zhang
    • 1
  • Qinghua Hu
    • 1
  • Yucan Zhou
    • 1
  • Xiaoxue Wang
    • 1
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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