Tackling business intelligence with bioinspired deep learning

Fombellida Vetas, Juan and Martín Rubio, Irene and Torres Alegre, Santiago and Andina de la Fuente, Diego (2018). Tackling business intelligence with bioinspired deep learning. "Neural computing and applications", v. 29 (n. 4); pp. 1-8. ISSN 0941-0643. https://doi.org/10.1007/s00521-018-3377-5.

Description

Title: Tackling business intelligence with bioinspired deep learning
Author/s:
  • Fombellida Vetas, Juan
  • Martín Rubio, Irene
  • Torres Alegre, Santiago
  • Andina de la Fuente, Diego
Item Type: Article
Título de Revista/Publicación: Neural computing and applications
Date: February 2018
ISSN: 0941-0643
Volume: 29
Subjects:
Freetext Keywords: Business intelligence, Business data, Bioinspired systems, Metaplasticity, Deep learning, MLP; AMMLP, AMP, Artificial neural network
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

More information

Item ID: 54998
DC Identifier: http://oa.upm.es/54998/
OAI Identifier: oai:oa.upm.es:54998
DOI: 10.1007/s00521-018-3377-5
Official URL: https://link.springer.com/article/10.1007%2Fs00521-018-3377-5
Deposited by: Memoria Investigacion
Deposited on: 28 May 2019 15:31
Last Modified: 29 May 2019 09:43
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