Classification in CPN using homogeneity based cluster re-arrangement

The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification algorithms for the original CPN classification algorithm to reduce the misclassification error in the regions of uncertain decisions. The accuracy of the proposed algorithm is tested with a case study.