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I would like to classify my data set into several classes which have a size relation (between classes) using supervised learning. For example let's say I'd like to classify subjects in order to predict their shirts' size.

My features would be stuff measured like:

 BMI, height, weight, etc...

The classes would be:

 XXS,XS,S,M,L,XL,XXL,XXXL

The classes have a relationship between them i.e.: If my true value is M and the algorithm predicts S is better than predicting XXL. Which supervised classification algorithm would you use in such a case?

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It seems that the features linearly reflect the prediction results.

Thus, a fully connected neural network with one softmax output layer and no hidden layers would be predictive enough of the classes, and this is a good start. If not performing well, add a hidden layer before the softmax output layer, and the performance would be good enough.

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  • $\begingroup$ It's not really what I've asked. I'm not looking what is the relationship between the features and the classes (this could be a non linear function). I'm really looking for an algorithm which can separate a case where the false prediction is close to the real class and cases where a false prediction is far from the real class. One more example: let's say the real class is XL. I would like an optimization function which would favor a L prediction over XS prediction. Both predictions are FALSE but because the classes are related we can say the L prediction is better. $\endgroup$ – AR_ Jul 25 '17 at 11:34
  • $\begingroup$ I see, now I understand your question. And one approach is to design a way for new labels that reflect class similarities. For example, one annotated instance with XL label is originally labeled with XL probability 1 and other labels with probability; the new label probabilities can be XL 0.8, L 0.1, and XXL 0.1, or M 0.01, L 0.09, XL 0.8, XXL 0.09, XXXL 0.01. A new model trained with new label probabilities can better reflect class similarities. $\endgroup$ – Tom Jul 25 '17 at 11:53

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