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Incremental mechanism of attribute reduction based on discernible relations for dynamically increasing attribute

  • Degang Chen
  • Lianjie DongEmail author
  • Jusheng Mi
Foundations

Abstract

Rough set is a data evaluation methodology to take care of uncertainty in data. Attribute reduction with rough set goals to achieve a compact and informative attribute set for a given data sets, and incremental mechanism is reasonable selection for attribute reduction in dynamic data sets. This paper focuses on introducing incremental mechanism to develop effective incremental algorithm during the arrival of new attributes in terms of approach of discerning samples. The traditional definition of discernibility matrix is improved first to address fewer samples to be discerned. Based on this improvement, discernible relation is developed for every attribute and utilized to characterize attribute reduction. For dynamic data sets with the dynamically increasing of attributes, an incremental mechanism is introduced to judge and ignore unnecessary new arriving attributes. For necessary new arriving attributes, the original reduct is updated in terms of updating of discernible relations instead of information granular or information entropy. The efficiency and effectiveness of developed incremental algorithm based on this mechanism is demonstrated through experimental comparisons in this paper in terms of running time.

Keywords

Rough set Attribute reduction Discernible relation Incremental mechanism 

Notes

Acknowledgements

This work is supported by the fund of North China Electric Power University, National Key R&D Program of China and the Fundamental Research Funds for the Central Universities (2018YFC0831404, 2018YFC0830605, 2018QN050).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics and PhysicsNorth China Electric Power UniversityBeijingChina
  2. 2.School of Control and Computer EngineeringNorth China Electric Power UniversityBeijingChina
  3. 3.College of ScienceHebei Agricultural UniversityBaodingChina
  4. 4.College of Mathematics and Information ScienceHebei Normal UniversityShijiazhuangChina

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