Abstract
Within the neural computing field, Fuzzy Cognitive Maps (FCMs) are attractive simulation tools to model dynamic systems by means of well-defined neural concepts and causal relationships, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle by experts. Recently, we proposed a symbolic FCM scheme (termed FCM-TFN) in which both weights and activation values are described by triangular fuzzy numbers. In spite of the promising results, the model’s performance in solving prediction problems remains uncertain. In this paper, we explore the prediction capabilities of the FCM-TFN model in pattern classification and concluded that our method is able to perform well when compared with traditional classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Benavoli, A., Corani, G., Mangili, F.: Should we really use post-hoc tests based on mean-ranks? J. Mach. Learn. Res. 17, 1–10 (2016)
Chen, C.: Extension of the topsis for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114, 1–9 (2000)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Dodurka, F., Yesil, E., Urbas, L.: Causal effect analysis for fuzzy cognitive maps designed with non-singleton fuzzy numbers. Neurocomputing 232, 122–132 (2017)
Dujmovic, J., Larsen, H.: Generalized conjunction/disjunction. Int. J. Approximate Reasoning 46, 423–446 (2007)
Eshelman, L.J.: The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. Found. Genet. Algorithms 1, 265–283 (1991)
Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K., Bello, R.: A review on methods and software for fuzzy cognitive maps. Artif. Intell. Rev. (2017)
Frias, M., Filiberto, Y., Nápoles, G., García-Socarrás, Y., Vanhoof, K., Bello, R.: Fuzzy cognitive maps reasoning with words based on triangular fuzzy numbers. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds.) MICAI 2017. LNCS (LNAI), vol. 10632, pp. 197–207. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02837-4_16
Gónzalez, M.P., De La Rosa, C.G.B., Moran, F.J.C.: Fuzzy cognitive maps and computing with words for modeling project portfolio risks interdependencies. Int. J. Innov. Appl. Stud. 15(4), 737–742 (2016)
John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers (1995)
Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24, 65–75 (1986)
Lipton, Z.: The mythos of model interpretability. arxiv preprint arxiv:160603490 (2016)
Molina, D., Pandolfi, D., Villagra, A., Leguizamón, G.: Applying CHC algorithms on radio network design for wireless communication. In: XX Congreso Argentino de Ciencias de la Computación (Buenos Aires 2014) (2014)
Nápoles, G., Mosquera, C., Falcon, R., Grau, I., Bello, R., Vanhoof, K.: Fuzzy-rough cognitive networks. Neural Netw. 97, 19–27 (2018)
Papakostas, G., Koulouriotis, D.: Classifying patterns using fuzzy cognitive maps. In: Glykas, M. (ed.) Fuzzy Cognitive Maps, vol. 247, pp. 291–306. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-03220-2_12
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods- Support Vector Learning. MIT Press, Cambridge (1998)
Quinlan, R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Rickard, J.T., Aisbett, J., Yager, R.R.: Computing with words in fuzzy cognitive maps. In: Proceedings of World Conference on Soft Computing, pp. 1–6 (2015)
Rickard, J., Aisbett, J., Yager, R., Gibbon, G.: Fuzzy weighted power means in evaluation decisions. In: 1st World Symposium on Soft Computing (2010)
Rickard, J., Aisbett, J., Yager, R., Gibbon, G.: Linguistic weighted power means: comparison with the linguistic weighted average. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), pp. 2185–2192 (2011)
Rosemblatt, F.: Principles of Neurodynamics. Spartan Books, New York (1962)
Rosero-Montalvo, P.D., et al.: Sign language recognition based on intelligent glove using machine learning techniques. In: 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), pp. 1–5 (2018)
Saleh, S.H., Rivas, S.D.L., Gómez, A.M.M., Mohsen, F.S., Vá zquez, M.L.: Knowledge representation using fuzzy cognitive maps and hesitant fuzzy linguistic term sets. Int. J. Innov. Appl. Stud. 17(1), 312–319 (2016)
Su, W., Peng, W., Zeng, S., Pen, B., Pand, T.: A method for fuzzy group decision making based on induced aggregation operators and euclidean distance. Int. Trans. Oper. Res. 20, 579–594 (2013)
Van, L., Pedrycz, W.: A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst. 11, 229–241 (1983)
Witten, I.H., Frank, E., Hall, M., Pal, C.: Data Mining: Practical Machine Learning Tools and Techniques (2017)
Zadeh, L.A.: Outline of a new approach to the analysis of complex systems ad decision processes. IEEE Trans. Syst. Man Cybern. SMC–3(1), 28–44 (1973)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Frias, M., Nápoles, G., Filiberto, Y., Bello, R., Vanhoof, K. (2019). A Preliminary Study on Symbolic Fuzzy Cognitive Maps for Pattern Classification. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-31019-6_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31018-9
Online ISBN: 978-3-030-31019-6
eBook Packages: Computer ScienceComputer Science (R0)