Accuracy, Precision, Recall, Sensitivity, Specificity, Kappa are the measures that can be derived from confusion matrix and AUC and Gini can be derived from ROC. Is there any other way to evaluate the performance of the classifiers.
Each one of these evaluation metrics has its own reason, I think you have chosen almost all the important metrics. You may think about Box plots, Violin plots, scatter plots, Kernel density estimation.. etc
Quantity disagreement and allocation disagreement :
Pontius, R.G., Jr.; Millones, M. Death to kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2011, 32, 440–4429