Abstract
This chapter is focused on the use of Fuzzy Cognitive Maps (FCMs) in classifying patterns, as alternative to the traditional classifiers such as neural networks or even as collaborators, in achieving better classification capabilities. By defining the classification procedure as the equilibrium point achieved by applying common inference laws, a FCM can simulate a typical classifier that maps a set of inputs to specific output values.
The classification capabilities of the FCM classifiers are studied in several pattern classification problems, while the ability of the FCM to store knowledge about the problem in hand is investigated in conjunction to the nodes’ type of activation function and the inference law used. Appropriate experiments are taken place, in order to analyze the behavior of the FCM-based classifiers, in well known benchmark problems.
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References
Boutalis, Y., Kottas, T.L., Christodoulou, M.: Adaptive etimation of fuzzy cognitive maps with proven stability and parameter convergence. IEEE Transactions on Fuzzy Systems 17, 874–889 (2009)
Bueno, S., Salmeron, J.L.: Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications 36, 5221–5229 (2009)
Chytas, P., Glykas, M., Staikouras, C., Valiris, G.: Performance measurement in a greek financial institute using the balanced scorecard. Journal of Measuring Business Excellence 10, 87–98 (2006)
Ghazanfari, M., Alizadeh, S., Fathian, M., Koulouriotis, D.E.: Comparing simulated annealing and genetic algorithm in learning FCM. Applied Mathematics and Computation 192, 56–68 (2007)
Glykas, M., Xirogiannis, G.: A soft knowledge modeling approach for geographically dispersed financial organizations. Soft Computing 9, 579–593 (2004)
Haykin, S.: Neural Networks – a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1999)
Khan, M.S., Quaddus, M.: Group decision support using fuzzy cognitive maps for causal reasoning. Group Decision and Negotiation 13, 463–480 (2004)
Kosko, B.: Fuzzy Cognitive Maps. International Journal Man-Machine Studies 24, 65–75 (1986)
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M.: Learning fuzzy cognitive maps using evolution strategies: a novel schema for modeling and simulating high-level behavior. IEEE Congr. Evolutionary Computation, 364–371 (2001a)
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M.: A fuzzy cognitive map-based stock market model: synthesis, analysis and experimental results. In: IEEE International Conference on Fuzzy Systems (FUZZ 2001), vol. 1, pp. 465–468 (2001b)
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M.: Realism in fuzzy cognitive maps: incorporating synergies and conditional effects. In: IEEE International Conference on Fuzzy Systems (FUZZ 2001), vol. 3, pp. 1179–1182 (2001c)
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M.: Anamorphosis of fuzzy cognitive maps for operation in ambiguous and multi-stimulus real world environments. In: IEEE International Conference on Fuzzy Systems (FUZZ 2001), vol. 3, pp. 1156–1159 (2001d)
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M., Antonidakis, E.N., et al.: Efficiently modeling and controlling complex dynamic systems using evolutionary fuzzy cognitive maps. International Journal of Computational Cognition 1, 41–65 (2003) (invited Paper)
Koulouriotis, D.E.: Investment analysis and decision making in markets using adaptive fuzzy causal relationships. Operational Research: an International Journal 4, 213–233 (2004)
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M., Zopounidis, C.D.: Development of dynamic cognitive networks as complex systems approximators: validation in financial time series. Applied Soft Computing 5, 157–179 (2005)
Langlet, F., Abdulkader, H., Roviras, D., Mallet, A., et al.: Comparison of neural network adaptive predistortion techniques for satellite down links. In: Proceedings of International Joint Conference on Neural Networks, pp. 709–714 (2001)
Pajares, G., de la Cruz, J.M.: Fuzzy cognitive maps for stereovision matching. Pattern Recognition 39, 2101–2114 (2006)
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)
Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Active Hebbian learning algorithm to train fuzzy cognitive maps. International Journal of Approximate Reasoning 37, 219–249 (2004)
Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.S., Groumpos, P.P., et al.: Fuzzy cognitive maps learning using particle swarm intelligence. Journal of Intelligence Information Systems 25, 95–121 (2005)
Papakostas, G.A., Karras, D.A., Mertzios, B.G., Boutalis, Y.S.: An efficient feature extraction methodology for computer vision applications using wavelet compressed Zernike moments. ICGST International Journal on Graphics, Vision and Image Processing, Special Issue: Wavelets and Their Applications SI1, 5–15 (2005)
Papakostas, G.A., Boutalis, Y.S., Koulouriotis, D.E., Mertzios, B.G.: A first study of pattern recognition by using fuzzy cognitive maps. In: 13th International Workshop on Systems, Signals and Image Processing (IWSSIP 2006), pp. 369–374 (2006)
Papakostas, G.A., Boutalis, Y.S., Koulouriotis, D.E., Mertzios, B.G.: Fuzzy cognitive maps for pattern recognition applications. International Journal of Pattern Recognition and Artificial Intelligence 22, 1461–1468 (2008)
Stach, W., Kurgan, L., Pedrycz, W., Reformar, M.: Genetic learning of fuzzy cognitive Maps. Fuzzy Sets and Systems 153, 371–401 (2005)
Stach, W., Kurgan, L.A., Pedrycz, W.: Numerical and linguistic prediction of time series with the use of Fuzzy Cognitive Maps. IEEE Transactions on Fuzzy Systems 16, 61–72 (2008)
Tsadiras, A.K.: Comparing the inference capabilities of Binary, Trivalent and Sigmoid fuzzy cognitive maps. Information Sciences 178, 3880–3894 (2008)
UCI-Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html
Xirogiannis, G., Stefanou, J., Glykas, M.: A fuzzy cognitive map approach to support urban design. Expert Systems with Applications 26, 257–268 (2004a)
Xirogiannis, G., Glykas, M.: Fuzzy cognitive maps in business analysis and performance driven change. IEEE Transactions on Engineering Management 51, 334–351 (2004b)
Xirogiannis, G., Glykas, M.: Intelligent modeling of e-business maturity. Expert Systems with Applications 32, 687–702 (2007)
Xirogiannis, G., Chytas, P., Glykas, M., Valiris, G.: Intelligent impact assessment of HRM to the shareholder value. Expert Systems with Applications 35, 2017–2031 (2008)
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Papakostas, G.A., Koulouriotis, D.E. (2010). Classifying Patterns Using Fuzzy Cognitive Maps. In: Glykas, M. (eds) Fuzzy Cognitive Maps. Studies in Fuzziness and Soft Computing, vol 247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03220-2_12
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DOI: https://doi.org/10.1007/978-3-642-03220-2_12
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