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The Discretization of Continuous Attributes Based on Compatibility Rough Set and Genetic Algorithm

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New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1711))

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Abstract

Most of the existing discretization approaches discrete each continuous attribute independently, without considering the discretization results of other continuous attributes. Therefore, some unreasonable and superfluous discretization split points are usually created. Based on compatibility rough set model and genetic algorithm, a global discretization approach has been provided. The experimental results indicate that the global discretization approach proposed can significantly decrease the number of discretization split points and the number of rules, but increase the predictive accuracy of the classifier.

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References

  1. De Jong, K.: Analysis of the Behavior of a Class of Genetic Adaptive System. Ph.D. Thesis, Department of Computer and Communications Sciences, University of Michigan, Ann Arbor, ML (1975)

    Google Scholar 

  2. Fayyad, U., Irani, K.B.: Multi-Interval Discretization of Continuous Attributes as Preprocessing for Classification Learning. In: Proceedings of the 13th International Join Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann Publishers, San Francisco (1993)

    Google Scholar 

  3. Grefenstette, J.J.: Technical Report CS-83-11, Computer Science Department, Vanderbilt University (1984)

    Google Scholar 

  4. Hong, J.R.: Learning from Examples and a Multi-Purpose Learning System AE5. Chinese Journal of Computers 12, 98–105 (1989)

    Google Scholar 

  5. Kerber, R.: ChiMerge: Discretization of Numeric Attributes. In: Proceedings 10th National Conference on Artificial Intelligence, pp. 123–127. MIT Press, Cambridge (1992)

    Google Scholar 

  6. Krawiec, K., Slowinski, R., Vanderpooten, D.: Construction of Rough Classifiers Based on Application of a Similarity Relation. In: Proceedings of the 4th International Workshop on Rough Sets, Fuzzy Sets and Machine Discovery, Tokyo, Japan (1996)

    Google Scholar 

  7. Kretowski, M., Stepaniuk, J.: Decision System Based on Tolerance Rough Sets. In: Proceedings of the 4th International Workshop on Intelligent Information Systems, Augustow, Poland (1995)

    Google Scholar 

  8. Lenarcik, A., Piasta, Z.: Minimizing the Number of Rules in Deterministic Rough Classifiers. In: Lin, Y.Y., Wildberge, A.M. (eds.) Soft Computing, San Diego, pp. 32–35 (1995)

    Google Scholar 

  9. Nguyen, H.S.: Discretization of Real Value Attribute: Boolean Reasoning Approach. Ph.D. Thesis, Warsaw University (1997)

    Google Scholar 

  10. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)

    MATH  Google Scholar 

  11. Mollestad, T., Skowron, A.: A Rough Set Framework for Data Mining of Propositional Default Rules. In: Michalewicz, M., Raś, Z.W. (eds.) ISMIS 1996. LNCS, vol. 1079. Springer, Heidelberg (1996)

    Google Scholar 

  12. Murphy, P.M.: UCI Repository of Machine Learning Database and Domain Theories, At http://www.ics.uci.edu/~mlearn/ML/Repository.html

  13. Slowinski, R., Vanderpooten, D.: Similarity Relation as a Basis for Rough Approximations. In: Proceedings of the Second Annual Joint Conference on Information Sciences, Wrightsville Beach, N. Carolina, USA, pp. 249–250 (1995)

    Google Scholar 

  14. Shan, N., Hamilton, H.J., Ziarko, W., Cercone, N.: Discretization of Continuous Valued Attributes in Attribute-Value Systems. In: Proceedings of the fourth International Workshop on Rough Sets, Fuzzy Sets and Machine Discovery, Tokyo, Japan, pp. 74–81 (1996)

    Google Scholar 

  15. Wu, X.D.: A Bayesian Discretizer for Real-Valued Attributes. The Computer Journal 39, 688–691 (1996)

    Article  Google Scholar 

  16. Yao, Y.Y., Wong, S.K.M.: Generalization of Rough Sets Using Relationships Between Attribute Values. In: Proceedings of the Second Annual Joint Conference on Information Sciences, Wrightsville Beach, N. Carolina, USA, pp. 30–33 (1995)

    Google Scholar 

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Authors and Affiliations

  1. Dept. of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Lixin Sun & Wen Gao

Authors
  1. Lixin Sun
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  2. Wen Gao
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Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Professor Emeritus, University of Tokyo,  

    Setsuo Ohsuga

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© 1999 Springer-Verlag Berlin Heidelberg

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Sun, L., Gao, W. (1999). The Discretization of Continuous Attributes Based on Compatibility Rough Set and Genetic Algorithm. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_23

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  • DOI: https://doi.org/10.1007/978-3-540-48061-7_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66645-5

  • Online ISBN: 978-3-540-48061-7

  • eBook Packages: Springer Book Archive

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