A Rough Set Classifier Based on Discretization and Attribute Selection

  • Yingjuan Sun
  • Dongbing PuEmail author
  • Dongbing Gu
  • John Q. Gan
  • Kun Yang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


Rough set is useful for classification in learning model. The main advantage of rough set is to find out the relativity among attributes directly from attributes of data without any preliminary or additional information. A rough set classifier based on discretization and attribute selection is proposed in this paper. Our rough set classifying algorithm give a full consider about condition attribute significance during the process of rule forming. We verified our algorithm on five well-known UCI machine learning data sets. The experiment results are expressed by mean of accuracies. At last, we compare our experiment results with the classical algorithms of other two references [8] and [9]. Results prove proposed algorithm is better than them. It can get higher classification accuracy, lower breakpoints and rules in all data sets of our experiments.


Classification Discretization Interval division Attribute significance 10-fold cross validation 



This work is supported by the Project of Research on Science and Technology of Jilin Education Ministry of China under Grant No. 2014249, No. 2015367 and No. 2013250, the Special Project of Jilin Province Industrial Technology Research and Development of China under Grant No. 2014Y101, No. 2019C052, the Research Foundation Project of Changchun normal University of China under Grant No. 2017015 and the financial support from the program of China Scholarship Council, No. 201408220056.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yingjuan Sun
    • 1
    • 2
  • Dongbing Pu
    • 3
    Email author
  • Dongbing Gu
    • 2
  • John Q. Gan
    • 2
  • Kun Yang
    • 2
  1. 1.College of Computer Science and TechnologyChangchun Normal UniversityChangchunChina
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK
  3. 3.School of Information Science and TechnologyNortheast Normal UniversityChangchunChina

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