[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

A Classifier Ensemble Method for Fuzzy Classifiers

  • Conference paper
Fuzzy Systems and Knowledge Discovery (FSKD 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4223))

Included in the following conference series:

Abstract

In this paper, a classifier ensemble method based on fuzzy integral for fuzzy classifiers is proposed. The object of this method is to reduce subjective factor in building a fuzzy classifier, and to improve the classification recognition rate and stability for classification system. For this object, a method of determining fuzzy integral density based on membership matrix is proposed, and the classifier ensemble algorithm based on fuzzy integral is introduced. The method of selecting classifier sets is also presented. The proposed method is evaluated by the comparison of experiments with standard data sets and the existed classifier ensemble methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ludmila, I., Kuncheva: How good are fuzzy If-Then classifiers? IEEE Transactions on Systems, Man, and Cybernetics 30(4), 501–509 (2000)

    Article  Google Scholar 

  2. Cordon, O., del Jesus, M.J., Herrera, F.: A proposal on reasoning methods in fuzzy rule-based classification systems. Int. J. of Approximate Reasoning 20(1), 21–45 (1999)

    Google Scholar 

  3. Wierzechon, S.T.: On fuzzy measure and fuzzy integral. In: Fuzzy information and decision processes, pp. 78–86. North-Holland, New York (1982)

    Google Scholar 

  4. Schapire, R.E.: The strength of weak learn ability. Machine Learning 5(2), 197–227 (1990)

    Google Scholar 

  5. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  6. Turner, K., Gosh, J.: Error correlation and error reduction in ensemble classifiers. Dept. of ECE, University of Texas, Texas (1996)

    Google Scholar 

  7. Partridge, D.: Network generalization differences quantified. Neural Networks (9), 263–271 (1996)

    Article  Google Scholar 

  8. Partridge, D., Yates, W.B.: Engineering Multiversion Neural-Net Systems. Neural Computation (8), 869–893 (1998)

    Article  Google Scholar 

  9. Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

  10. Zhi-Hua, Z., Shi-Fu, C.: Neural Network Ensemble. Chinese Journal of Computers 25(1), 1–8 (2005)

    Google Scholar 

  11. Xie-Dong, Z.: Fuzzy Information Managing and Application, pp. 174–176. Science Publishing Company, Beijing (2003)

    Google Scholar 

  12. Ai-Min, Y.: The Model Research on Fuzzy Classification. Doctor’s Degree Paper of Fudan University (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Hunan University of Technology, ZhuZhou, 412008, China

    Ai-min Yang, Yong-mei Zhou & Min Tang

  2. College of Information Science & Engineering, Central South University, ChangSha, 410083, China

    Yong-mei Zhou

Authors
  1. Ai-min Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong-mei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University,, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Life Science Research Center, School of Electronic Engineering, Xidian University,, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  3. School of Electrical and Electronic Engineering, Xidian University, 710071, Xi’an, China

    Guanming Shi

  4. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, Queensland, Australia

    Xue Li

  5. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, Am., Zhou, Ym., Tang, M. (2006). A Classifier Ensemble Method for Fuzzy Classifiers. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_97

Download citation

  • DOI: https://doi.org/10.1007/11881599_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45916-3

  • Online ISBN: 978-3-540-45917-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation