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Credit Evaluation Model and Applications Based on Probabilistic Neural Network

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Computational Intelligence and Security (CIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3801))

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Abstract

The paper introduces the method of probabilistic neural network (PNN) and its classifying principle. It constructs two PNN structures which are used to recognize both the two patterns and the three patterns respectively. The structure of the two patterns classification of PNN is used to classify the 106 listed companies of China in 2000 into two groups. The classification accuracy rate is 87.74%. The structure of the three patterns classification of PNN is used to classify the 96 listed companies of China in 2000 into three groups. The classification accuracy rate is 85.42%.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Jinan University, Guangzhou, 510632, China

    Sulin Pang

Authors
  1. Sulin Pang
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Editor information

Editors and Affiliations

  1. Microelectronic Instiute, Xidian University, 710071, Xi’an, China

    Yue Hao

  2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  3. School of Computer Science and Technology, Xidian University, Xi’an, China

    Yuping Wang

  4. Department of Computer Science, Hong Kong Baptist University, Hong Kong,  

    Yiu-ming Cheung

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  6. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  7. Key Laboratory of Computer Networks and Information Security (Ministry of Education), Xidian University, 710071, Xi’an, China

    Jianfeng Ma

  8. National Laboratory of Antennas and Microwave Technology, Xidian University, 710071, Xi’an, Shanxi, P.R. China

    Yong-Chang Jiao

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© 2005 Springer-Verlag Berlin Heidelberg

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Pang, S. (2005). Credit Evaluation Model and Applications Based on Probabilistic Neural Network. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_153

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  • DOI: https://doi.org/10.1007/11596448_153

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30818-8

  • Online ISBN: 978-3-540-31599-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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