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## An Optimized RBF-Neural Network for Breast Cancer Classification

#### Siouda Roguia [1] , Nemissi Mohamed [2]

##### 25 72

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.

K-means, Radial Basis Function Networks, Classification, Neural Networks, Particle Swarm Optimization
• 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)
Primary Language en Computer Science, Interdisciplinary Application Articles Orcid: 0000-0002-0251-9880Author: Siouda Roguia (Primary Author)Institution: 8 Mai 1945 GuelmaCountry: Algeria Author: Nemissi MohamedInstitution: University of Guelma
 Bibtex @research article { ijiam531728, journal = {International Journal of Informatics and Applied Mathematics}, issn = {}, eissn = {2667-6990}, address = {International Society of Academicians}, year = {2018}, volume = {1}, pages = {24 - 34}, doi = {}, title = {An Optimized RBF-Neural Network for Breast Cancer Classification}, key = {cite}, author = {Roguia, Siouda and Mohamed, Nemissi} } 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. Retrieved from http://dergipark.org.tr/ijiam/issue/43831/531728 MLA Roguia, S , Mohamed, N . "An Optimized RBF-Neural Network for Breast Cancer Classification". International Journal of Informatics and Applied Mathematics 1 (2018): 24-34 Chicago Roguia, S , Mohamed, N . "An Optimized RBF-Neural Network for Breast Cancer Classification". International Journal of Informatics and Applied Mathematics 1 (2018): 24-34 RIS TY - JOUR T1 - An Optimized RBF-Neural Network for Breast Cancer Classification AU - Siouda Roguia , Nemissi Mohamed Y1 - 2018 PY - 2018 N1 - DO - T2 - International Journal of Informatics and Applied Mathematics JF - Journal JO - JOR SP - 24 EP - 34 VL - 1 IS - 1 SN - -2667-6990 M3 - UR - Y2 - 2019 ER - EndNote %0 International Journal of Informatics and Applied Mathematics An Optimized RBF-Neural Network for Breast Cancer Classification %A Siouda Roguia , Nemissi Mohamed %T An Optimized RBF-Neural Network for Breast Cancer Classification %D 2018 %J International Journal of Informatics and Applied Mathematics %P -2667-6990 %V 1 %N 1 %R %U 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.