Abstract
This study evaluates the performance provided by a Blume-Emery-Griffiths neural network (BEGNN) for two datasets of corruption indicators, namely the Corruption Perceptions Index and the Global Corruption Barometer. Bi-lineal and bi-quadratic terms are added to the Hamiltonian of the model, as well as for the order parameters to measure the network retrieval efficiency. The network is tested for different noise levels of the patterns’ initial state during the retrieval phase in order to measure the robustness of the network and its basin of attraction. The network connectivity is diluted periodically and its performance is tested for different levels of dilution. The network is analyzed in terms of the pattern load, mixing the real corruption patterns with random patterns in order to assess the change from retrieval to non-retrieval phases.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bollé, D., Dominguez, D.R.C., Erichsen Jr., R., Korutcheva, E., Theumann, W.K.: Time evolution of the extremely diluted Blume-Emery-Griffiths neural network. Phys. Rev. E 68(6), 062901 (2003)
Bollé, D., Dominguez, D., Amari, S.I.: Mutual information of sparsely coded associative memory with self-control and ternary neurons. Neural Netw. 13(4–5), 455–462 (2000)
Dominguez, D., Pantoja, O., González, M.: Mapping the global offshoring network through the panama papers. In: Rocha, Á., Guarda, T. (eds.) ICITS 2018. AISC, vol. 721, pp. 407–416. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_39
Dominguez, D.R.C., Korutcheva, E.: Three-state neural network: from mutual information to the Hamiltonian. Phys. Rev. E 62(2), 2620 (2000)
Doria, F., Erichsen Jr., R., González, M., Rodríguez, F.B., Sánchez, Á., Dominguez, D.: Structured patterns retrieval using a metric attractor network: application to fingerprint recognition. Physica A Stat. Mech. Appl. 457, 424–436 (2016)
González, M., Dominguez, D., Rodríguez, F.B., Sanchez, A.: Retrieval of noisy fingerprint patterns using metric attractor networks. Int. J. Neural Syst. 24(07), 1450025 (2014)
González, M., Dominguez, D., Sánchez, Á.: Learning sequences of sparse correlated patterns using small-world attractor neural networks: an application to traffic videos. Neurocomputing 74(14–15), 2361–2367 (2011)
González, M., Dominguez, D., Sánchez, Á., Rodríguez, F.B.: Capacity and retrieval of a modular set of diluted attractor networks with respect to the global number of neurons. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2017. LNCS, vol. 10305, pp. 497–506. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59153-7_43
González, M., Dominguez, D., Sánchez, Á., Rodríguez, F.B.: Increase attractor capacity using an ensembled neural network. Expert Syst. Appl. 71, 206–215 (2017)
González, M., del Mar Alonso-Almeida, M., Avila, C., Dominguez, D.: Modeling sustainability report scoring sequences using an attractor network. Neurocomputing 168, 1181–1187 (2015)
González, M., del Mar Alonso-Almeida, M., Dominguez, D.: Mapping global sustainability report scoring: a detailed analysis of Europe and Asia. Qual. Quant. 52, 1–15 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Qaiser, B., Nadeem, S., Siddiqi, M.U., Siddiqui, A.F.: Relationship of social progress index (SPI) with gross domestic product (GDP PPP per capita): the moderating role of corruption perception index (CPI). Pakistan J. Eng. Technol. Sci. 7(1), 61–76 (2018)
Rose-Ackerman, S., Palifka, B.J.: Corruption and Government: Causes, Consequences, and Reform. Cambridge University Press, Cambridge (2016)
Srivastava, S.C., Teo, T.S., Devaraj, S.: You can’t bribe a computer: dealing with the societal challenge of corruption through ICT. MIS Q. 40(2), 511–526 (2016)
Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems, pp. 2553–2561 (2013)
TI-CPI: Transparency International Corruption Perceptions Index (2018). https://www.transparency.org/research/cpi/overview
TI-GCB: Transparency International Global Corruption Barometer 2004 (2018). https://www.transparency.org/whatwedo/publication/gcb_2004
TI-GCB: Transparency International Global Corruption Barometer 2015/16/17 (2018). https://www.transparency.org/research/gcb/gcb_2015_16
Villoria, M., Van Ryzin, G.G., Lavena, C.F.: Social and political consequences of administrative corruption: a study of public perceptions in Spain. Public Adm. Rev. 73(1), 85–94 (2013)
Yoon, J., Klasen, S.: An application of partial least squares to the construction of the social institutions and gender index (SIGI) and the corruption perception index (CPI). Soc. Ind. Res. 138(1), 61–88 (2018)
Acknowledgments
This work has been supported by MINECO TIN2014-54580-R, TIN2017-84452-R, and by UAM-Santander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
González, M., Dominguez, D., Jerez, G., Pantoja, O. (2018). Periodically Diluted BEGNN Model of Corruption Perception. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-05918-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05917-0
Online ISBN: 978-3-030-05918-7
eBook Packages: Computer ScienceComputer Science (R0)