- International Journal of Informatics and Applied Mathematics
- Vol: 1 Issue: 1
- An Optimized RBF-Neural Network for Breast Cancer Classification
An Optimized RBF-Neural Network for Breast Cancer Classification
Authors : Siouda ROGUİA, Nemissi Mohamed
Pages : 24-34
View : 6 | Download : 4
Publication Date : 2018-12-20
Article Type : Research
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.Keywords : K-means, Radial Basis Function Networks, Classification, Neural Networks, Particle Swarm Optimization