An examination on the effect of CVNN parameters while classifying the real-valued balanced and unbalanced data

摘要

In this study, a Complex-Valued Neural Network is designed to investigate the effects of the mapping angle and the learning rate on both imbalanced and balanced data. Symmetry detection problems with 3 different lengths are handled as the imbalanced data with event rates of 0.25, 0.125 and 0.0675. In order to make the data balanced, the symmetric members of the training set are resampled. The effects of the learning rate and the mapping angle are investigated for 3 different activation functions. The performance of the CVNN is measured using confusion matrix. 4-fold cross validation is used to validate the results. The results show that the CVNN is a strong tool to classify both the real valued imbalanced and balanced data with the right mapping angle and the learning rate that suit the selected activation function.