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Attribute reduction in interval-valued information systems based on information entropies

  • Jian-hua Dai
  • Hu Hu
  • Guo-jie Zheng
  • Qing-hua Hu
  • Hui-feng Han
  • Hong Shi
Article

Abstract

Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.

Keywords

Rough set theory Interval-valued data Attribute reduction Entropy 

CLC number

TP18 

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Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jian-hua Dai
    • 1
    • 2
  • Hu Hu
    • 2
  • Guo-jie Zheng
    • 2
  • Qing-hua Hu
    • 1
  • Hui-feng Han
    • 2
  • Hong Shi
    • 1
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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