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Year 2018, Volume: 1 Issue: 1, 24 - 34, 20.12.2018

Abstract

References

  • Reference1 Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on. pp. 39–43. IEEE (1995)
  • Reference2 Kennedy, J., Eberhart, R.: Particle swarm optimization, proceedings of ieee international conference on neural networks (icnn’95) in (1995)
  • Reference3 Lichman, M.: Uci machine learning repository. irvine, ca: University of california, school of information and computer science. URL http://archive. ics. uci. edu/ml (2013)
  • Reference4 Mert, A., Kılıc¸, N., Bilgili, E., Akan, A.: Breast cancer detection with reduced feature set. Computational and mathematical methods in medicine 2015 (2015)
  • Reference5 Qasem, S., Shamsuddin, S.: Generalization improvement of radial basis function network based on multi-objective particle swarm optimization. J. Artif. Intell 3(1), 1–16 (2010)
  • Reference6 Rumelhart, D., Hinton, G., Williams, R.: Learning internal representation by error propagation, parallel distributed processing: exploration in the microstructure of cognition. MIT press Cambridge pp. 318–362 (1986)
  • Reference7 Venkatesan, A.S., Parthiban, L.: A novel optimized adaptive learning approach of rbf on biomedical data sets. Research Journal of Applied Sciences, Engineering and Technology 8(4), 541–547 (2014)
  • Reference8 Zarbakhsh, P., Addeh, A., et al.: Breast cancer tumor type recognition using graph feature selection technique and radial basis function neural network with optimal structure. Journal of cancer research and therapeutics 14(3), 625 (2018)

An Optimized RBF-Neural Network for Breast Cancer Classification

Year 2018, Volume: 1 Issue: 1, 24 - 34, 20.12.2018

Abstract

This paper introduces an optimized RBF-Neural Network for breast cancer classification. The study is based on the optimization of the network through three learning phases. In the first phase, K-means clustering method is used to define RBFs centers. In the second phase, Particle Swarm Optimization is used to optimize RBFs widths. In this phase, a pseudo inverse solution is used to calculate the output weights. Finally, in the third phase, the back-propagation algorithm is used for fine-tuning the obtained parameters, namely centers, widths and output weights. The back-propagation is then initialized with the obtained parameters instead of a random initialization. To evaluate the performance of the proposed method, tests were performed using the Wisconsin Diagnostic Breast Cancer database. The proposed system was compared with a network trained only with BP and a network trained with K-means + PSO. The results obtained are promising compared to other advanced methods and the proposed learning method gives better results by combining these three methods.


References

  • Reference1 Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on. pp. 39–43. IEEE (1995)
  • Reference2 Kennedy, J., Eberhart, R.: Particle swarm optimization, proceedings of ieee international conference on neural networks (icnn’95) in (1995)
  • Reference3 Lichman, M.: Uci machine learning repository. irvine, ca: University of california, school of information and computer science. URL http://archive. ics. uci. edu/ml (2013)
  • Reference4 Mert, A., Kılıc¸, N., Bilgili, E., Akan, A.: Breast cancer detection with reduced feature set. Computational and mathematical methods in medicine 2015 (2015)
  • Reference5 Qasem, S., Shamsuddin, S.: Generalization improvement of radial basis function network based on multi-objective particle swarm optimization. J. Artif. Intell 3(1), 1–16 (2010)
  • Reference6 Rumelhart, D., Hinton, G., Williams, R.: Learning internal representation by error propagation, parallel distributed processing: exploration in the microstructure of cognition. MIT press Cambridge pp. 318–362 (1986)
  • Reference7 Venkatesan, A.S., Parthiban, L.: A novel optimized adaptive learning approach of rbf on biomedical data sets. Research Journal of Applied Sciences, Engineering and Technology 8(4), 541–547 (2014)
  • Reference8 Zarbakhsh, P., Addeh, A., et al.: Breast cancer tumor type recognition using graph feature selection technique and radial basis function neural network with optimal structure. Journal of cancer research and therapeutics 14(3), 625 (2018)
There are 8 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Siouda Roguia 0000-0002-0251-9880

Nemissi Mohamed This is me

Publication Date December 20, 2018
Acceptance Date March 13, 2019
Published in Issue Year 2018 Volume: 1 Issue: 1

Cite

APA Roguia, S., & Mohamed, N. (2018). An Optimized RBF-Neural Network for Breast Cancer Classification. International Journal of Informatics and Applied Mathematics, 1(1), 24-34.
AMA Roguia S, Mohamed N. An Optimized RBF-Neural Network for Breast Cancer Classification. IJIAM. December 2018;1(1):24-34.
Chicago Roguia, Siouda, and Nemissi Mohamed. “An Optimized RBF-Neural Network for Breast Cancer Classification”. International Journal of Informatics and Applied Mathematics 1, no. 1 (December 2018): 24-34.
EndNote Roguia S, Mohamed N (December 1, 2018) An Optimized RBF-Neural Network for Breast Cancer Classification. International Journal of Informatics and Applied Mathematics 1 1 24–34.
IEEE S. Roguia and N. Mohamed, “An Optimized RBF-Neural Network for Breast Cancer Classification”, IJIAM, vol. 1, no. 1, pp. 24–34, 2018.
ISNAD Roguia, Siouda - Mohamed, Nemissi. “An Optimized RBF-Neural Network for Breast Cancer Classification”. International Journal of Informatics and Applied Mathematics 1/1 (December 2018), 24-34.
JAMA Roguia S, Mohamed N. An Optimized RBF-Neural Network for Breast Cancer Classification. IJIAM. 2018;1:24–34.
MLA Roguia, Siouda and Nemissi Mohamed. “An Optimized RBF-Neural Network for Breast Cancer Classification”. International Journal of Informatics and Applied Mathematics, vol. 1, no. 1, 2018, pp. 24-34.
Vancouver Roguia S, Mohamed N. An Optimized RBF-Neural Network for Breast Cancer Classification. IJIAM. 2018;1(1):24-3.

International Journal of Informatics and Applied Mathematics