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
Birincil Dil | İngilizce |
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Konular | Yazılım Mühendisliği (Diğer) |
Bölüm | Makaleler |
Yazarlar | |
Yayımlanma Tarihi | 20 Aralık 2018 |
Kabul Tarihi | 13 Mart 2019 |
Yayımlandığı Sayı | Yıl 2018 Cilt: 1 Sayı: 1 |
International Journal of Informatics and Applied Mathematics