I have made some architectures of neural networks in order to classify proteins in three categories. I have calculated true positive, true negative, false negative, false positive, Mathews, sensitivity, specificity, accuracy for each category. My question is what is the right way to evaluate the architecture. Except for the c-index, could I calculate a cumulative sensitivity, accuracy etc?or should I evaluate separately for each category? Thank you!
Wow, I would just go for accuarcy as the singular most important measure, but be careful of sampling bias.
If the classification training had an unbiased sample* and you have an accuracy of >90%, in general ANN that wouldn't be great, but in bioinformatcs thats really good. If your training comprises sampling bias that gets tricky and you either need to accomodate that in your accuarcy ROC etc ... or else retrain to eliminate the sample bias.
You can account for all of them of course by wrapping everything into a single analysis.
*, by this I mean that each of the 3 classifications comprises 33% in each training set. If this is badly skewed, this requires consideration and gets complicated. If it is precisely 1/3 each then 'accurary' is the key stat.