Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm

Reza Narimani; Ahmad Narimani

doi:doi:10.11648/j.acis.20130105.12

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Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm

Reza Narimani, Ahmad Narimani

Published in Automation, Control and Intelligent Systems (Volume 1, Issue 5)

Received: 18 August 2013 Published: 20 September 2013

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Abstract

This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). The PNN-DDA has two parameters called positive and negative threshold. This learning algorithm trains very quickly. Thus it makes sense that we use a meta-heuristic algorithm such as particle swarm optimization to optimize these parameters. When using the meta-heuristic algorithm such PSO, the tuning process of parameters is implemented wisely. Thus in this paper we also obtained optimum threshold. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the proposed model. The result shows that this new hybrid algorithm outperforms the most common used algorithm such as multi-layer neural network.

Published in	Automation, Control and Intelligent Systems (Volume 1, Issue 5)
DOI	10.11648/j.acis.20130105.12
Page(s)	103-112
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Probabilistic Neural Network Particle Swarm Optimization, Dynamic Decay Algorithm, Classification

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APA Style

Reza Narimani, Ahmad Narimani. (2013). Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm. Automation, Control and Intelligent Systems, 1(5), 103-112. https://doi.org/10.11648/j.acis.20130105.12

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ACS Style

Reza Narimani; Ahmad Narimani. Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm. Autom. Control Intell. Syst. 2013, 1(5), 103-112. doi: 10.11648/j.acis.20130105.12

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AMA Style

Reza Narimani, Ahmad Narimani. Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm. Autom Control Intell Syst. 2013;1(5):103-112. doi: 10.11648/j.acis.20130105.12

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@article{10.11648/j.acis.20130105.12,
  author = {Reza Narimani and Ahmad Narimani},
  title = {Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {5},
  pages = {103-112},
  doi = {10.11648/j.acis.20130105.12},
  url = {https://doi.org/10.11648/j.acis.20130105.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130105.12},
  abstract = {This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). The PNN-DDA has two parameters called positive and negative threshold. This learning algorithm trains very quickly. Thus it makes sense that we use a meta-heuristic algorithm such as particle swarm optimization to optimize these parameters.  When using the meta-heuristic algorithm such PSO, the tuning process of parameters is implemented wisely. Thus in this paper we also obtained optimum threshold. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the proposed model. The result shows that this new hybrid algorithm outperforms the most common used algorithm such as multi-layer neural network.},
 year = {2013}
}

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TY  - JOUR
T1  - Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm
AU  - Reza Narimani
AU  - Ahmad Narimani
Y1  - 2013/09/20
PY  - 2013
N1  - https://doi.org/10.11648/j.acis.20130105.12
DO  - 10.11648/j.acis.20130105.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 103
EP  - 112
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130105.12
AB  - This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). The PNN-DDA has two parameters called positive and negative threshold. This learning algorithm trains very quickly. Thus it makes sense that we use a meta-heuristic algorithm such as particle swarm optimization to optimize these parameters.  When using the meta-heuristic algorithm such PSO, the tuning process of parameters is implemented wisely. Thus in this paper we also obtained optimum threshold. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the proposed model. The result shows that this new hybrid algorithm outperforms the most common used algorithm such as multi-layer neural network.
VL  - 1
IS  - 5
ER  -

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Author Information

Reza Narimani

Department of Financial Engineering, University of Economic Sciences, Tehran, Iran
Ahmad Narimani

Department of Economics, University of AllamehTabatabae'i, Tehran, Iran

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APA Style

Reza Narimani, Ahmad Narimani. (2013). Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm. Automation, Control and Intelligent Systems, 1(5), 103-112. https://doi.org/10.11648/j.acis.20130105.12

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ACS Style

Reza Narimani; Ahmad Narimani. Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm. Autom. Control Intell. Syst. 2013, 1(5), 103-112. doi: 10.11648/j.acis.20130105.12

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AMA Style

Reza Narimani, Ahmad Narimani. Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm. Autom Control Intell Syst. 2013;1(5):103-112. doi: 10.11648/j.acis.20130105.12

Copy | Download

@article{10.11648/j.acis.20130105.12,
  author = {Reza Narimani and Ahmad Narimani},
  title = {Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {5},
  pages = {103-112},
  doi = {10.11648/j.acis.20130105.12},
  url = {https://doi.org/10.11648/j.acis.20130105.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130105.12},
  abstract = {This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). The PNN-DDA has two parameters called positive and negative threshold. This learning algorithm trains very quickly. Thus it makes sense that we use a meta-heuristic algorithm such as particle swarm optimization to optimize these parameters.  When using the meta-heuristic algorithm such PSO, the tuning process of parameters is implemented wisely. Thus in this paper we also obtained optimum threshold. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the proposed model. The result shows that this new hybrid algorithm outperforms the most common used algorithm such as multi-layer neural network.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm
AU  - Reza Narimani
AU  - Ahmad Narimani
Y1  - 2013/09/20
PY  - 2013
N1  - https://doi.org/10.11648/j.acis.20130105.12
DO  - 10.11648/j.acis.20130105.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 103
EP  - 112
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130105.12
AB  - This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). The PNN-DDA has two parameters called positive and negative threshold. This learning algorithm trains very quickly. Thus it makes sense that we use a meta-heuristic algorithm such as particle swarm optimization to optimize these parameters.  When using the meta-heuristic algorithm such PSO, the tuning process of parameters is implemented wisely. Thus in this paper we also obtained optimum threshold. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the proposed model. The result shows that this new hybrid algorithm outperforms the most common used algorithm such as multi-layer neural network.
VL  - 1
IS  - 5
ER  -

Copy | Download