I'm trying to classify a binary sample with Keras and I would like to classify as many correctly as possible, while ignore the ones where the model is not sure.
The fully connected Nerual network currencly achieves around 65% but I would like to get a higher result of correctly classified ones, while ignoring the ones where the model is uncertain.
Is there a way to tell Keras to simply ignore the ones where the model is uncertain and achieve a higher accuracy that way? Or is there a network design that could achieve this, for example feeding the result of the network striaght into a second part of it which then decides whether the prediction is likely accurate or not?
One way I was thinking of achieving this is by building a second neural network on top of it that decides based on the result of the first network and all the input data of it, whether the classification will be correct or not. Would that work, and if yes, is there no more elegant way of achieving this in one go, such as directly having the results feeding into a second part of the network that then decides if the prediction is likley accurate or not?