2 Output Neural Network for Large Classificaiton Tasks : I've been thinking about this for a while but I cant seem to find any data on it. When classifying with a neural network you usually assign regions of the output neuron's activation function to a specific class, e.g. For tanh you could set 0.8 for class 1 and -0.8 for class 2. This is all well and good if you have up to 3 classes (the third class can be around zero), but when you have more classes things can become tricky.

Take an example where you are classifying football players based on their statistics. An attacking midfield player and a striker have similar statistics, but if you assign them to regions on opposite sides of the activation function, the accuracy of the classifier is surely harmed.

Would it not be easier to have a 2 output neural network that outputs an arbitrary x and a y value such that the class regions could be represented in 2D rather than 1D? You could essentially have a circle, cut into the number of classes you want and have the centre of each slice as the target value for the class. This seems like a good way to classify to me but the lack of relevant data on the subject is leading me to believe there are easier ways to perform classification with a higher number of classes (say 6 classes for example). The reason I ask is because I am trying to classify football players in certain positions based on their stats. You can see a scatter plot of the top 2 principal component scores for players below.

[![enter image description here][1]][1]


  [1]: https://i.stack.imgur.com/F6a0D.png