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Biological Plausibility in an Artificial Neural Network Applied to Real Predictive Tasks

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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Abstract

Biologically plausible artificial neural networks represent a promising novel approach in bio-inspired computational systems. In these systems, the models are based on existing knowledge of neurophysiological processing principles. Research in this field has increased in the last few years and has generated new viewpoints, propositions and models that are closer to the known features of the human brain. Some researchers have recently focused their studies on this innovative field in order to establish a consensus on what an artificial neural network is in the domain of biological realism. Domain specific synthetic data sets are generally used in the evaluation of those artificial neural networks because they simulate predictive tasks and potential problems caused by human intervention. This paper deals with the analysis of influence of the anomalies generated by human intervention in credit approval process. Such anomalies modify real classification, performance and accuracy. In this analysis, we evaluated a real data set that represents human actions over personal credit approval and fraud identification by using a biologically more plausible artificial neural network proposal.

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Authors and Affiliations

  1. Laboratório de Computação Bio-inspirada (BioCom), Departamento de Ciências da Computação, Instituto de Ciências Matemáticas e de Computação – ICMC, Universidade de São Paulo, Campus de São Carlos, Caixa Postal 668, 13560-970, São Carlos, SP, Brazil

    Alberione Braz da Silva & João Luís Garcia Rosa

Authors
  1. Alberione Braz da Silva
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  2. João Luís Garcia Rosa
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2013 Springer-Verlag Berlin Heidelberg

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da Silva, A.B., Rosa, J.L.G. (2013). Biological Plausibility in an Artificial Neural Network Applied to Real Predictive Tasks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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