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An Automatic Approach for Generation of Fuzzy Membership Functions

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Agents and Artificial Intelligence (ICAART 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10162))

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Abstract

Eliciting representative membership functions is one of the fundamental steps in applications of fuzzy theory. This paper investigates an unsupervised approach that incorporates variable bandwidth mean-shift and robust statistics for generating fuzzy membership functions. The approach automatically learns the number of representative functions from the underlying data distribution. Given a specific membership function, the approach then works out the associated parameters of the specific membership function. Our evaluation of the proposed approach consists of comparisons with two other techniques in terms of (i) parameterising MFs for attributes with different distributions, and (ii) classification performance of a fuzzy rule set that was developed using the parameterised output of these techniques. This evaluation involved its application using the trapezoidal and the triangular membership functions. Results demonstrate that the generated membership functions can better separate the underlying distributions and classifiers constructed using the proposed method of generating membership function outperformed three other classifiers that used different approaches for parameterisation of the attributes.

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References

  1. Medasani, S., Kim, J., Krishnapuram, R.: An overview of membership function generation techniques for pattern recognition. Int. J. Approx. Reason. 19(3), 391–417 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  2. Seki, H.: Fuzzy inference based non-daily behavior pattern detection for elderly people monitoring system. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 6187–6192. IEEE (2009)

    Google Scholar 

  3. Moeinzadeh, H., et al.: Improving classification accuracy using evolutionary fuzzy transformation. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers. ACM (2009)

    Google Scholar 

  4. Amaral, T.G., Crisóstomo, M.M.: Automatic helicopter motion control using fuzzy logic. In: 2001 The 10th IEEE International Conference on Fuzzy Systems. IEEE (2001)

    Google Scholar 

  5. Tang, K., Man, K., Chan, C.: Fuzzy control of water pressure using genetic algorithm. In: Proceedings of the Safety, Reliability and Applications of Emerging Intelligent Control Technologies (2014)

    Google Scholar 

  6. Takagi, H., Hayashi, I.: NN-driven fuzzy reasoning. Int. J. Approx. Reason. 5(3), 191–212 (1991)

    Article  MATH  Google Scholar 

  7. Pazhoumand-Dar, H., Lam, C.P., Masek, M.: A novel fuzzy based home occupant monitoring system using kinect cameras. In: IEEE 27th International Conference on Tools with Artificial Intelligence, Vietri sul Mare, Italy (2015)

    Google Scholar 

  8. Kuok, C.M., Fu, A., Wong, M.H.: Mining fuzzy association rules in databases. ACM Sigmod Rec. 27(1), 41–46 (1998)

    Article  Google Scholar 

  9. Doctor, F., Iqbal, R., Naguib, R.N.: A fuzzy ambient intelligent agents approach for monitoring disease progression of dementia patients. J. Ambient Intell. Humaniz. Comput. 5(1), 147–158 (2014)

    Article  Google Scholar 

  10. Castellano, G., Fanelli, A., Mencar, C.: Generation of interpretable fuzzy granules by a double-clustering technique. Arch. Control Sci. 12(4), 397–410 (2002)

    MathSciNet  MATH  Google Scholar 

  11. Hubert, M., Vandervieren, E.: An adjusted boxplot for skewed distributions. Comput. Stat. Data Anal. 52(12), 5186–5201 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  12. Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001. IEEE (2001)

    Google Scholar 

  13. Pazhoumand-Dar, H., Lam, C.P., Masek, M.: Automatic generation of fuzzy membership functions using adaptive mean-shift and robust statistics. In: Proceedings of the 8th International Conference on Agents and Artificial Intelligence, Italy (2016)

    Google Scholar 

  14. Rousseeuw, P.J., Hubert, M.: Robust statistics for outlier detection. Wiley Interdisc. Rev. Data Mining Knowl. Discov. 1(1), 73–79 (2011)

    Article  Google Scholar 

  15. Brys, G., Hubert, M., Struyf, A.: A robust measure of skewness. J. Comput. Graphical Stat. 13(4), 996–1017 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  16. Sheather, S.J., Jones, M.C.: A reliable data-based bandwidth selection method for kernel density estimation. J. R. Stat. Soc. Ser. B (Methodol.) 53, 683–690 (1991)

    MathSciNet  MATH  Google Scholar 

  17. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy c-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984)

    Article  Google Scholar 

  18. Tajbakhsh, A., Rahmati, M., Mirzaei, A.: Intrusion detection using fuzzy association rules. Appl. Soft Comput. 9(2), 462–469 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Science, Edith Cowan University, Perth, Australia

    Hossein Pazhoumand-Dar, Chiou Peng Lam & Martin Masek

Authors
  1. Hossein Pazhoumand-Dar
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  2. Chiou Peng Lam
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  3. Martin Masek
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Corresponding author

Correspondence to Chiou Peng Lam .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  2. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Pazhoumand-Dar, H., Lam, C.P., Masek, M. (2017). An Automatic Approach for Generation of Fuzzy Membership Functions. In: van den Herik, J., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2016. Lecture Notes in Computer Science(), vol 10162. Springer, Cham. https://doi.org/10.1007/978-3-319-53354-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-53354-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53353-7

  • Online ISBN: 978-3-319-53354-4

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