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Tackling business intelligence with bioinspired deep learning

  • S.I. : IWINAC 2015
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Abstract

To tackle the complex problem of providing business intelligence solutions based on business data, bioinspired deep learning has to be considered. This paper focuses on the application of artificial metaplasticity learning in business intelligence systems as an alternative paradigm of achieving a deeper information extraction and learning from arbitrary size data sets. As a case study, artificial metaplasticity multilayer perceptron applied to the automation of credit approval decision based on collected client data is analyzed, showing its potential and improvements over the state-of-the-art techniques. This paper successfully introduces the relevant novelty that the artificial neural network itself estimates the pdf of the input data to be used in the metaplasticity learning, so it is much closer to the biologic reality than previous implementations of artificial metaplasticity.

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Author notes

  1. J. Fombellida

    Present address: ETSIT Universidad Politécnica de Madrid, 28040, Madrid, Spain

Authors and Affiliations

  1. Group for Automation in Signals and Communications, ETSIT Universidad Politécnica de Madrid, Madrid, Spain

    J. Fombellida, S. Torres-Alegre & D. Andina

  2. Group for Automation in Signals and Communications, ETSIDI Universidad Politécnica de Madrid, Madrid, Spain

    I. Martín-Rubio

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  1. J. Fombellida
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  2. I. Martín-Rubio
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  3. S. Torres-Alegre
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  4. D. Andina
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Correspondence to J. Fombellida.

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Fombellida, J., Martín-Rubio, I., Torres-Alegre, S. et al. Tackling business intelligence with bioinspired deep learning. Neural Comput & Applic 32, 13195–13202 (2020). https://doi.org/10.1007/s00521-018-3377-5

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  • DOI: https://doi.org/10.1007/s00521-018-3377-5

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