Abstract
In this work, we developed a Differential Evolution (DE) trained Fuzzy Cognitive Map (FCM) for predicting the bank efficiency. We developed two modes of training namely (i) sequential and (ii) batch modes. We compared the DE trained FCM models with the conventional Hebbian training in both modes. We employed Mean Absolute Percentage Error (MAPE) as an error measure while predicting the efficiency from Return on Assets (ROA), Return on Equity (ROE), Profit Margin (PM), Utilization of Assets (UA), and Expenses Ratio (ER). We employed 5x2-fold cross-validation framework. In the first case i.e. sequential mode of training, the DE trained FCM statistically outperformed the Hebbian trained FCM and in the second case i.e. batch mode of training, DE trained FCM is statistically the same as the Hebbian trained FCM. To break the tie in the batch mode, the training time is compared where DE trained FCM turned to be 19% faster than the Hebbian trained FCM. The proposed model can be applied to solving similar banking and insurance problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Boutalis, Y., Kottas, T., Christodoulou, M.: Adaptive estimation of fuzzy cognitive maps with proven stability and parameter convergence. IEEE Trans. Fuzzy Syst. 17(4), 874–889 (2009)
Boutalis, Y., Kottas, T., Christodoulou, M.: On the existence and uniqueness of solutions for the concept values in fuzzy cognitive maps. In: 47th IEEE Conference on Decision and Control, Cancun, Mexico, pp. 98–104. IEEE (2008)
Bueno, S., Salmeron, J.L.: Benchmarking main activation functions in fuzzy cognitive maps. Expert Syst. Appl. 36(3), 5221–5229 (2009)
Dickerson, J.A., Kosko, B.: Virtual worlds as fuzzy cognitive maps. Presence: Teleoperators Virtual Environ. 3(2), 173–189 (1994)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co., Boston (1989)
Jayashree, L.S., Palakkal, N., Papageorgiou, E.I., Papageorgiou, K.: Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region. Neural Comput. Appl. 26(8), 1963–1978 (2015)
Jayashree, L., Lakshmi Devi, R., Papandrianos, N., Papageorgiou, E.I.: Application of fuzzy cognitive map for geospatial dengue outbreak risk prediction of tropical regions of Southern India. Intell. Decis. Technol. 12(2), 231–250 (2018)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: International Conference on Neural Networks (ICNN 1995), Piscataway, NJ, pp. 1942–1948. IEEE (1995)
Krishna, G.J., Ravi, V.: Evolutionary computing applied to customer relationship management: a survey. Eng. Appl. Artif. Intell. 56, 30–59 (2016)
Krishna, G.J., Ravi, V.: Evolutionary computing applied to solve some operational issues in banks. In: Datta, S., Davim, J. (eds.) Optimization in Industry. Management and Industrial Engineering, pp. 31–53. Springer, Cham (2019)
Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24(1), 65–75 (1986)
Papageorgiou, E., Markinos, A., Gemtos, T.: Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application. Appl. Soft Comput. 11(4), 3643–3657 (2011)
Papageorgiou, E.I.: Learning algorithms for fuzzy cognitive maps: a review study. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(2), 150–163 (2012)
Papageorgiou, E.I., Salmeron, J.L.: A review of fuzzy cognitive maps research during the last decade. IEEE Trans. Fuzzy Syst. 21(1), 66–79 (2013)
Parsopoulos, K.E., Papageorgiou, E.I., Groumpos, P.P., Vrahatis, M.N.: Evolutionary computation techniques for optimizing fuzzy cognitive maps in radiation therapy systems. In: Deb, K. (ed.) Genetic and Evolutionary Computation Conference, pp. 402–413. Springer, Heidelberg (2004)
Pramodh, C., Ravi, V., Nagabhushanam, T.: Indian banks’ productivity ranking via data envelopment analysis and fuzzy multi-attribute decision-making hybrid. Int. J. Inf. Decis. Sci. 1(1), 44 (2008)
Robins, A.: Sequential learning in neural networks: a review and a discussion of pseudorehearsal based methods. Intell. Data Anal. 8(3), 301–322 (1997)
Stach, W., Kurgan, L., Pedrycz, W.: Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps. In: IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), Hong Kong, China, pp. 1975–1981. IEEE, June 2008
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2010)
Dasgupta, S., Das, S., Biswas, A., Abraham, A.: On stability and convergence of the population-dynamics in differential evolution. AI Commun. 22(1), 1–20 (2009)
Ali, M., Pant, M., Abraham, A.: Simplex differential evolution. Acta Polytechnica Hungarica 6(5), 95–115 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jaya Krishna, G., Smruthi, M., Ravi, V., Shandilya, B. (2020). Differential Evolution Trained Fuzzy Cognitive Map: An Application to Modeling Efficiency in Banking. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-16660-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16659-5
Online ISBN: 978-3-030-16660-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)