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Fuzzy Clustering in Classification Using Weighted Features

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Fuzzy Sets and Systems — IFSA 2003 (IFSA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2715))

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Abstract

This paper proposes a fuzzy classification/regression method based on an extension of classical fuzzy clustering algorithms, by weighting the features during cluster estimation. By translating the importance of each feature using weights, the classifier can lead to better results. The proposed method is applied to target selection, where the goal is to maximize profit obtained from the clients. A real-world application shows the effectiveness of the proposed approach.

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References

  1. C. L. Bauer. A direct mail customer purchase model. Journal of Direct Marketing, 2:16–24, 1988.

    Article  Google Scholar 

  2. J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function. Plenum Press, New York, 1981.

    MATH  Google Scholar 

  3. J. S. Cramer. The LOGIT Model: an introduction for economists. Edward Arnold, Kent, 1991.

    Google Scholar 

  4. D. E. Gustafson and W. C. Kessel. Fuzzy clustering with a fuzzy covariance matrix. In Proceedings IEEE CDC, pages 761–766, San Diego, USA, 1979.

    Google Scholar 

  5. U. Kaymak. Fuzzy target selection using RFM variables. In Proceedings of joint 9th IFSA World Congress and 20th NAFIPS Int. Conference, pages 1038–1043, Vancouver, Canada, July 2001.

    Google Scholar 

  6. U. Kaymak and M. Setnes. Fuzzy clustering with volume prototypes and adaptive cluster merging. IEEE Transactions on Fuzzy Systems, 10(6):705–712, December 2002.

    Article  Google Scholar 

  7. S. Madeira and J.M. Sousa. Comparison of target selection methods in direct marketing. In Proceedings of European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems, Eunite’2002, pages 333–338, Albufeira, Portugal, September 2002.

    Google Scholar 

  8. J. M. Sousa, U. Kaymak, and S. Madeira. A comparative study of fuzzy target selection methods in direct marketing. In Proceedings of 2002 IEEE World Congress on Computational Intelligence, WCCI’2002, FUZZ-IEEE’2002, pages 1–6, Paper 1251, Hawaii, USA, May 2002.

    Google Scholar 

  9. J.M. Sousa and U. Kaymak. Fuzzy Decision Making in Modeling and Control. World Scientific, Singapore and UK, December 2002.

    MATH  Google Scholar 

  10. H. Timm, C. Doring, and R. Kruse. Fuzzy cluster analysis of partially missing data. In Proceedings of European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems, Eunite’2002, pages 426–431, Albufeira, Portugal, September 2002.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Instituto Superior Técnico, Dept. of Mechanical Engineering, GCAR/IDMEC, Technical University of Lisbon, 1049-001, Lisbon, Portugal

    Lourenço P. C. Bandeira & João M. C. Sousa

  2. Faculty of Economics, Erasmus University of Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, the Netherlands

    Uzay Kaymak

Authors
  1. Lourenço P. C. Bandeira
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  2. João M. C. Sousa
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  3. Uzay Kaymak
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey

    Taner Bilgiç

  2. Dept. of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000, Gent, Belgium

    Bernard De Baets

  3. Department of Electrical and Electronics Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey

    Okyay Kaynak

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© 2003 Springer-Verlag Berlin Heidelberg

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Bandeira, L.P.C., Sousa, J.M.C., Kaymak, U. (2003). Fuzzy Clustering in Classification Using Weighted Features. In: Bilgiç, T., De Baets, B., Kaynak, O. (eds) Fuzzy Sets and Systems — IFSA 2003. IFSA 2003. Lecture Notes in Computer Science, vol 2715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44967-1_67

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  • DOI: https://doi.org/10.1007/3-540-44967-1_67

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40383-8

  • Online ISBN: 978-3-540-44967-6

  • eBook Packages: Springer Book Archive

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