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Evolving Hierarchical RBF Neural Networks for Breast Cancer Detection

  • Yuehui Chen
  • Yan Wang
  • Bo Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

Hierarchical RBF networks consist of multiple RBF networks assembled in different level or cascade architecture. In this paper, an evolved hierarchical RBF network was employed to detect the breast cancel. For evolving a hierarchical RBF network model, Extended Compact Genetic Programming (ECGP), a tree-structure based evolutionary algorithm and the Differential Evolution (DE) are used to find an optimal detection model. The performance of proposed method was then compared with Flexible Neural Tree (FNT), Neural Network (NN), and RBF Neural Network (RBF-NN) by using the same breast cancer data set. Simulation results show that the obtained hierarchical RBF network model has a fewer number of variables with reduced number of input features and with the high detection accuracy.

Keywords

Breast Cancer Detection Breast Cancer Data Gaussian Radial Basis Function High Detection Accuracy Multiple Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuehui Chen
    • 1
  • Yan Wang
    • 1
  • Bo Yang
    • 1
  1. 1.School of Information Science and EngineeringJinan UniversityJinanP.R. China

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