An Optimized RBF-Neural Network for Breast Cancer Classification

Authors : Siouda ROGUİA, Nemissi Mohamed

Pages : 24-34

View : 12 | Download : 11

Publication Date : 2018-12-20

Article Type : Research Paper

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