Abstract
Fuzzy cognitive maps (FCMs) is a knowledge representation tool that can be exploited for predicting multivariate time-series. FCM model represents dependencies among data variables as a directed, weighted graph of fuzzy sets (concepts). This way, FCM can be easily interpreted or constructed by experts in contrary to black box knowledge representation methods. Since FCM is a parametric model, it can be trained using historical data. So far, the genetic algorithm has been used to solely optimize the weights of FCM leaving the rest of FCM parameters to be adjusted by experts. Previous studies have shown that the genetic algorithm can be also used not only for optimizing the weights but also for optimization of FCM transformation functions. The main idea presented in this chapter is to further extend FCM evolutionary learning process. Special focus is given on fuzzyfication and transformation function optimization, applied in each concept seperately, in order to improve the efficacy of time-series prediction. The proposed extended evolutionary optimization process was evaluated in a number of real medical data gathered from the internal care unit (ICU). Comparing this approach with other known genetic-based learning algorithms, less prediction errors were observed for this dataset.
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References
Efron, B.: Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78, 316–333 (1983)
Froelich, W., Juszczuk, P.: Predictive capabilities of adaptive and evolutionary fuzzy cognitive maps—a comparative study. In: Nguyen, N.T., Szczerbicki, E. (eds.) Intelligent Systems for Knowledge Management, Studies in Computational Intelligence, vol. 252, pp. 153–174. Springer, Berlin (2009)
Froelich, W., Wakulicz-Deja, A.: Medical diagnosis support by the application of associational fuzzy cognitive maps. Control Cybern. 39(2), 439–456 (2010)
Ghazanfari, M., Alizadeh, S.: Learning fcm with simulated annealing. In: Simulated Annealing, InTechOpen, Vienna (2008)
Huerga, A.V.: A balanced differential learning algorithm in fuzzy cognitive maps. In: Proceedings of the 16th International Workshop on Qualitative Reasoning, Barcelona, Spain, June 2002
Juszczuk, P., Froelich, W.: Learning fuzzy cognitive maps using a differential evolution algorithm. Pol. J. Environ. Stud. 12(3B), 108–112 (2009)
Kafetzis, A., McRoberts, N., Mouratiadou, I.: Using fuzzy cognitive maps to support the analysis of stakeholders views of water resource use and water quality policy. In: Glikas, M. (ed.) Fuzzy Cognitive Maps. Advances in Theory, Methodologies, Tools and Applications. Springer, Dordrecht (2010)
Kosko, B.: Differential hebbian learning. In: American Institute of Physics, Neural Networks for Computing, pp. 277–282, April (1986)
Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24(1), 65–75 (1986)
Liu, Z., Satur, R.: Contextual fuzzy cognitive map for decision support in geographic information systems. IEEE Trans. Fuzz. Syst. 5, 495–507 (1999)
Papageorgiou, E., Froelich, W.: Forecasting the state of pulmonary infection by the application of fuzzy cognitive maps. In: Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on, pp. 1–4. IEEE (2010)
Papageorgiou, E., Markinos, A., Gemtos, T.: Soft computing technique of fuzzy cognitive maps to connect yield defining parameters with yield in cotton crop production in central greece as a basis for a decision support system for precision agriculture application. In: Glykas, M. (ed.) Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools, Applications (2010)
Papageorgiou, E., Stylios, C.D., Groumpos, P.P.: Active hebbian learning algorithm to train fuzzy cognitive maps. Int. J. Approximate Reasoning 37(3), 219–249 (2004)
Papageorgiou, E.I.: A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl. Soft Comput. 11, 500–513 (2011)
Papageorgiou, E.I., Froelich, W.: Application of evolutionary fuzzy cognitive maps for prediction of pulmonary infections. IEEE Trans. Inf Technol. Biomed. 16(1), 143–149 (2012)
Papageorgiou, E.I., Froelich, W.: Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps. Neurocomputing 92, 28–35 (2012)
Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.D., Groumpos, P.P., Vrahatis, M.N.: Fuzzy cognitive maps learning using particle swarm optimization. J. Intell. Inf. Syst. 25, 95–121 (2005)
Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Fuzzy cognitive map learning based on nonlinear hebbian rule. In: Australian Conference on, Artificial Intelligence, pp. 256–268 (2003)
Song, H., Miao, C., Shen, Z., Roel, W., Maja, D., Francky, C.: Design of fuzzy cognitive maps using neural networks for predicting chaotic time series. Neural Netw. 23(10), 1264–1275 (2010)
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)
Papageorgiou, E.I.: Learning algorithms of Fuzzy Cognitive Maps-a review study. IEEE Trans. Sys. Man. Cybern. Part C. 42(2), 150–163 (2012).
Stach, W., Kurgan, L.A., Pedrycz, W.: Parallel learning of large fuzzy cognitive maps. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, pp. 1–6 (2007)
Stach, W., Kurgan, L.A., Pedrycz, W.: Numerical and linguistic prediction of time series with the use of fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 16(1), 61–72 (2008)
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Froelich, W., Papageorgiou, E.I. (2014). Extended Evolutionary Learning of Fuzzy Cognitive Maps for the Prediction of Multivariate Time-Series. In: Papageorgiou, E. (eds) Fuzzy Cognitive Maps for Applied Sciences and Engineering. Intelligent Systems Reference Library, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39739-4_7
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