ROC Analysis as a Useful Tool for Performance Evaluation of Artificial Neural Networks

  • Fikret Tokan
  • Nurhan Türker
  • Tülay Yıldırım
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


In many applications of neural networks, the performance of the network is given by the classification accuracy. While obtaining the classification accuracies, the total true classification is computed, but the number of classification rates of the classes and fault classification rates are not given. This would not be enough for a problem having fatal importance. As an implementation example, a dataset having fatal importance is classified by MLP, RBF, GRNN, PNN and LVQ networks and the real performances of these networks are found by applying ROC analysis.


Classification Accuracy Radial Basis Function Receiver Operating Characteristic Receiver Operating Characteristic Curve Radial Basis Function Network 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fikret Tokan
    • 1
  • Nurhan Türker
    • 1
  • Tülay Yıldırım
    • 1
  1. 1.Electrical and Electronics Faculty, Department of Electronics and Communication EngineeringYıldız Technical UniversityYıldız, IstanbulTurkey

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