Automatic Generation of Fuzzy Membership Functions using Adaptive Mean-shift and Robust Statistics

Hossein Pazhoumand-Dar, Chiou-Peng Lam, Martin Masek

Abstract

In this paper, an unsupervised approach incorporating variable bandwidth mean-shift and robust statistics is presented for generating fuzzy membership functions from data. The approach takes an attribute and automatically learns the number of representative functions from the underlying data distribution. Given a specific membership function, the approach also works out the associated parameters. The investigation here examines the application of approach using the triangular membership function. Results from partitioning of attributes confirm that the generated membership functions can better separate the underlying distributions when compared to a number of other techniques. Classification performance of fuzzy rule sets produced using four different methods of parameterizing the associated attributes is examined. We observed that the classifier constructed using the proposed method of generating membership function outperformed the 3 other classifiers that had used other methods of parameterizing the attributes.

References

  1. Amaral, T. G. & Crisóstomo, M. M. Automatic helicopter motion control using fuzzy logic. Fuzzy Systems, 2001. The 10th IEEE International Conference on, 2001. IEEE, 860-863.
  2. Brys, G., Hubert, M. & Struyf, A. 2004. A robust measure of skewness. Journal of Computational and Graphical Statistics, 13.
  3. Castellano, G., Fanelli, A. & Mencar, C. 2002. Generation of interpretable fuzzy granules by a double-clustering technique. Archives of Control Science, 12, 397-410.
  4. Comaniciu, D., Ramesh, V. & Meer, P. The variable bandwidth mean shift and data-driven scale selection. Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, 2001. IEEE, 438-445.
  5. Doctor, F., Iqbal, R. & Naguib, R. N. 2014. A fuzzy ambient intelligent agents approach for monitoring disease progression of dementia patients. Journal of Ambient Intelligence and Humanized Computing, 5, 147-158.
  6. Hubert, M. & Vandervieren, E. 2008. An adjusted boxplot for skewed distributions. Computational statistics & data analysis, 52, 5186-5201.
  7. Kuok, C. M., Fu, A. & Wong, M. H. 1998. Mining fuzzy association rules in databases. ACM Sigmod Record, 27, 41-46.
  8. Medasani, S., Kim, J. & Krishnapuram, R. 1998. An overview of membership function generation techniques for pattern recognition. International Journal of approximate reasoning, 19, 391-417.
  9. Moeinzadeh, H., Nasersharif, B., Rezaee, A. & Pazhoumand-Dar, H. Improving classification accuracy using evolutionary fuzzy transformation. Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, 2009. ACM, 2103- 2108.
  10. Pazhoumand-Dar, H., Lam, C. P. & Masek, M. A Novel Fuzzy Based Home Occupant Monitoring System Using Kinect Cameras. IEEE 27th International Conference on Tools with Artificial Intelligence, 2015 Vietri sul Mare, Italy. in press.
  11. Rousseeuw, P. J. & Hubert, M. 2011. Robust statistics for outlier detection. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1, 73-79.
  12. Seki, H. 2009. Fuzzy inference based non-daily behavior pattern detection for elderly people monitoring system. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. IEEE.
  13. Sheather, S. J. & Jones, M. C. 1991. A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society. Series B (Methodological), 683-690.
  14. Tajbakhsh, A., Rahmati, M. & Mirzaei, A. 2009. Intrusion detection using fuzzy association rules. Appl. Soft Comput., 9, 462-469.
  15. Takagi, H. & Hayashi, I. 1991. NN-driven fuzzy reasoning. International Journal of Approximate Reasoning, 5, 191-212.
  16. Tang, K., Man, K. & Chan, C. Fuzzy control of water pressure using genetic algorithm. Proceedings of the Safety, Reliability and Applications of Emerging Intelligent Control Technologies, 2014. 15-20.
Download


Paper Citation


in Harvard Style

Pazhoumand-Dar H., Lam C. and Masek M. (2016). 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 - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 160-171. DOI: 10.5220/0005751601600171


in Bibtex Style

@conference{icaart16,
author={Hossein Pazhoumand-Dar and Chiou-Peng Lam and Martin Masek},
title={Automatic Generation of Fuzzy Membership Functions using Adaptive Mean-shift and Robust Statistics},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={160-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005751601600171},
isbn={978-989-758-172-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Automatic Generation of Fuzzy Membership Functions using Adaptive Mean-shift and Robust Statistics
SN - 978-989-758-172-4
AU - Pazhoumand-Dar H.
AU - Lam C.
AU - Masek M.
PY - 2016
SP - 160
EP - 171
DO - 10.5220/0005751601600171