A Hybrid Prediction Model Integrating Fuzzy Cognitive Maps with Support Vector Machines

Panayiotis Christodoulou, Andreas Christoforou, Andreas S. Andreou

2017

Abstract

This paper introduces a new hybrid prediction model combining Fuzzy Cognitive Maps (FCM) and Support Vector Machines (SVM) to increase accuracy. The proposed model first uses the FCM part to discover correlation patterns and interrelationships that exist between data variables and form a single latent variable. It then feeds this variable to the SVM part to improve prediction capabilities. The efficacy of the hybrid model is demonstrated through its application on two different problem domains. The experimental results show that the proposed model is better than the traditional SVM model and also outperforms other widely used supervised machine-learning techniques like Weighted k-NN, Linear Discrimination Analysis and Classification Trees.

References

  1. Andreou, A.S., Mateou, N.H. & Zombanakis, G.A., 2005. Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps. Soft Computing, 9(3), pp.194-210.
  2. Asuncion, A. & Newman, D., 2007. UCI machine learning repository.
  3. Bueno, S. & Salmeron, J.L., 2009. Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications, 36(3 PART 1), pp.5221- 5229.
  4. Candanedo, L.M. & Feldheim, V., 2016. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy and Buildings, 112, pp.28-39.
  5. Cortes, C. & Vapnik, V., 1995. Support-Vector Networks. Machine Learning, 20(3), pp.273-297.
  6. Gou, J. et al., 2012. A new distance-weighted k-nearest neighbor classifier. J. Inf. Comput. Sci.
  7. Iakovidis, D.K. & Papageorgiou, E., 2011. Intuitionistic Fuzzy Cognitive Maps for Medical Decision Making. IEEE Transactions on Information Technology in Biomedicine, 15(1), pp.100-107.
  8. Kandasamy, W.B.V. & Smarandache, F., 2003. Fuzzy cognitive maps and neutrosophic cognitive maps, Infinite Study.
  9. Kosko, B., 1986. Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), pp.65-75.
  10. Kosko, B., 2010. Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications 1st ed. M. Glykas, ed., Springer-Verlag Berlin Heidelberg.
  11. Kosko, B., 1993. Fuzzy thinking: The new science of fuzzy logic, Hyperion Books.
  12. Kosko, B., 1992. Neural networks and fuzzy systems: a dynamical approach to machine intelligence. Englewood Cliffs. NJ: Prentice-Hall, 1(99), p.1.
  13. Loh, W.-Y., 2011. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), pp.14-23.
  14. Mohandes, M.A. et al., 2004. Support vector machines for wind speed prediction. Renewable Energy, 29(6), pp.939-947.
  15. Papageorgiou, E., 2013. Review study on Fuzzy Cognitive Maps and their applications during the last decade. Business Process Management, pp.828-835.
  16. Papageorgiou, E.I. & Poczeta, K., 2015. Application of fuzzy cognitive maps to electricity consumption prediction. In 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC). IEEE, pp. 1-6.
  17. Papageorgiou, E.I., Poczeta, K. & Laspidou, C., 2016. Hybrid model for water demand prediction based on fuzzy cognitive maps and artificial neural networks. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, pp. 1523-1530.
  18. Papageorgiou, E.I. & Salmeron, J.L., 2013. A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems, 21(1), pp.66- 79.
  19. Shin, K.-S., Lee, T.S. & Kim, H., 2005. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), pp.127-135.
  20. Smith, J.W. et al., 1988. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proceedings of the Annual Symposium on Computer Application in Medical Care, p.261.
  21. McLachlan, G., 2004. Discriminant analysis and statistical pattern recognition (Vol. 544). John Wiley & Sons.
  22. Papageorgiou, E., Georgoulas, G., Stylios, C., Nikiforidis, G. and Groumpos, P., 2006, October. Combining fuzzy cognitive maps with support vector machines for bladder tumor grading. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (pp. 515-523). Springer Berlin Heidelberg.
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Paper Citation


in Harvard Style

Christodoulou P., Christoforou A. and S. Andreou A. (2017). A Hybrid Prediction Model Integrating Fuzzy Cognitive Maps with Support Vector Machines . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 554-564. DOI: 10.5220/0006329405540564


in Bibtex Style

@conference{iceis17,
author={Panayiotis Christodoulou and Andreas Christoforou and Andreas S. Andreou},
title={A Hybrid Prediction Model Integrating Fuzzy Cognitive Maps with Support Vector Machines},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={554-564},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006329405540564},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Hybrid Prediction Model Integrating Fuzzy Cognitive Maps with Support Vector Machines
SN - 978-989-758-247-9
AU - Christodoulou P.
AU - Christoforou A.
AU - S. Andreou A.
PY - 2017
SP - 554
EP - 564
DO - 10.5220/0006329405540564