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Fuzzy ID3 Algorithm Based on Generating Hartley Measure

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Web Information Systems and Mining (WISM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6988))

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Abstract

Fuzzy decision tree induction algorithm is an important way with uncertain information. However, the current fuzzy decision tree algorithms do not systematically consider the impact of different fuzzy levels and simply make uncertainty treatment awareness into the selection of extended properties. To avoiding this problem, this paper establishes a generating Hartley measure model based on cut-standard, subsequently, proposes fuzzy ID3 algorithm based on generating Hartley measure model, finally, the results of the experiments indicates that the model is feasible and effective.

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References

  1. Davis, L.D.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  2. Quinlan, J.R.: Induction of decision trees. Machine Learning, 81–106 (1986)

    Google Scholar 

  3. Quinlan, J.R.: C4.5: Programs for Machine Learning, pp. 1–131. Morgan Kaufman, San Francisco (1993)

    Book  Google Scholar 

  4. Metha, M., Agrawal, R., Rissanen, J.: SLIQ: A Fast Scalable for Classifier for Data Mining. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 18–32. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  5. Shafer, J., Agrawal, R., Mehta, M.: SPRINT: A Scalable Parallel Classifier for Data Mining. In: Proceedings of the 22nd International Conference on Very Large Databases, pp. 544–555. Morgan Kauffman, San Mateo (1996)

    Google Scholar 

  6. Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets and System 69, 125–139 (1995)

    Article  MathSciNet  Google Scholar 

  7. Wang, X.G., Yeung, D.S., Tsang, E.C.C.: A comparative study on heuristic algorithms for generating fuzzy decision trees. IEEE Transactions on Systems. Man and Cybernet. Ics-part B: Cybernetics 31(2) (April 2001)

    Google Scholar 

  8. Zhang, X., Zhao, H.: Fuzzy decision fusion algorithm of uncertain information. Journal of Northeastern University 7(25), 657–660 (2004)

    Google Scholar 

  9. Zhai, J., Wang, H.: A fuzzy classification algorithm based on fuzzy entropy. Computer Engineering and Applications 20(46), 176–180 (2010)

    Google Scholar 

  10. Eyrvl, H.: Transmission of Information. Bell Systems Technical Journal 17, 535–563 (1928)

    Google Scholar 

  11. Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  12. Higashi, M., Klir, G.J.: Measures of uncertainty and information based on possibility distributions. Internat. J. Gen. Systems 9, 43–58 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  13. Umanol, M., Okamoto, H., Hatono, I., Tamura, H., Kawachi, F., Umedzu, S., Kinoshita, J.: Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems. In: IEEE Int. Conf. on Fuzzy Systems, June 26–29, pp. 2113–2118 (1994)

    Google Scholar 

  14. Zhai, J., Wang, X., Zhang, S.: The fusion of multi fuzzy decision tree based on fuzzy Integral. Computer Research and Development 3(46), 470–477 (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Economics and Management, Hebei University of Science and Technology, 050018, Shijiazhuang, China

    Fachao Li & Dandan Jiang

Authors
  1. Fachao Li
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  2. Dandan Jiang
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Editor information

Editors and Affiliations

  1. Department of Computer and Inforamtion Science, University of Macau, Av. Padre Tomás Pereira, Taipa, Macau, China

    Zhiguo Gong

  2. School of Computer, Shanghai University, 200444, Shanghai, China

    Xiangfeng Luo

  3. College of Computer and Software, Taiyuan University of Technology, 030024, Taiyuan, China

    Junjie Chen

  4. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  5. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Li, F., Jiang, D. (2011). Fuzzy ID3 Algorithm Based on Generating Hartley Measure. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23982-3_24

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  • DOI: https://doi.org/10.1007/978-3-642-23982-3_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23981-6

  • Online ISBN: 978-3-642-23982-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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