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Intuitionistic Fuzzy Neural Network: The Case of Credit Scoring Using Text Information

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Engineering Applications of Neural Networks (EANN 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 517))

Abstract

Intuitionistic fuzzy inference systems (IFISs) incorporate imprecision in the construction of membership functions present in fuzzy inference systems. In this paper we design intuitionistic fuzzy neural networks to adapt the antecedent and consequent parameters of IFISs. We also propose a mean of maximum defuzzification method for a class of Takagi-Sugeno IFISs and this method is compared with the center of area and basic defuzzification distribution operator. On credit scoring data, we show that the intuitionistic fuzzy neural network trained with gradient descent and Kalman filter algorithms outperforms the traditional ANFIS method.

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Authors and Affiliations

  1. Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic

    Petr Hájek & Vladimír Olej

Authors
  1. Petr Hájek
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  2. Vladimír Olej
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Correspondence to Petr Hájek .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Orestiada, Greece

    Lazaros Iliadis

  2. Coventry University, Coventry, United Kingdom

    Chrisina Jayne

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Hájek, P., Olej, V. (2015). Intuitionistic Fuzzy Neural Network: The Case of Credit Scoring Using Text Information. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_31

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  • DOI: https://doi.org/10.1007/978-3-319-23983-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23981-1

  • Online ISBN: 978-3-319-23983-5

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