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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)

Abstract

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.

Keywords

Classification Discretization Interval division Attribute significance 10-fold cross validation 

Notes

Acknowledgements

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.

References

  1. 1.
    Singh, N., Singh, P.: A novel Bagged Naïve Bayes-Decision Tree approach for multi-class classification problems. J. Intell. Fuzzy Syst. 36, 2261–2271 (2019)CrossRefGoogle Scholar
  2. 2.
    Geng, Z., Meng, Q., Bai, J., Chen, J., Han, Y., Wei, Q., Ouyang, Z.: A model-free Bayesian classifier. Inf. Sci. 482, 171–188 (2019)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Aviad, B., Roy, G.: Classification by clustering decision tree-like classifier based on adjusted clusters. Expert Syst. Appl. 38, 8220–8228 (2011)CrossRefGoogle Scholar
  4. 4.
    Chang, C.C., Chien, L.J., Lee, Y.J.: A framework for multi-classification via ternary smooth support vector machine. Pattern Recogn. 44, 1235–1244 (2011)CrossRefGoogle Scholar
  5. 5.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRefGoogle Scholar
  6. 6.
    Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the 12th International Conference on Machine Learning, pp. 194–202. Morgan Kaufmann Publishers (1995)Google Scholar
  7. 7.
    Jefferys, W.H., Berger, J.O.: Ockham’s razor and Bayesian analysis. Am. Sci. 80(1), 64–72 (1992)Google Scholar
  8. 8.
    Bhattacharya, G., Ghosh, K., Chowdhury, A.S.: An affinity-based new local distance function and similarity measure for kNN algorithm. Pattern Recogn. Lett. 33, 356–363 (2012)CrossRefGoogle Scholar
  9. 9.
    Lu, C.Y., Min, H., Gui, J., Zhu, L., Lei, Y.K.: Face recognition via weighted sparse representation. J. Vis. Commun. Image R. 24, 111–116 (2013)CrossRefGoogle Scholar
  10. 10.
    Wei, W., Liang, J.: Information fusion in rough set theory: an overview. Inf. Fusion 48, 107–118 (2019)CrossRefGoogle Scholar
  11. 11.
    Shi, Z., Xia, Y., Wu, F., Dai, J.: The discretization algorithm for rough data and its application to intrusion detection. J. Netw. 9(6), 1380 (2014)Google Scholar

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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