1
$\begingroup$

class-1 represents 0.01, class-i represents 0.01*i, class-100 represents 1.00.
Thus, when the classifier predicts the class-y and it should have predicted class-(y+1) there is a small error so we can accept class-y.
Is there a way to express this behaviour in a neural network? Maybe with a distribution or something?
PS: Not interested in regression.

Improve this question
$\endgroup$
4
  • $\begingroup$ Hi, welcome to Data Science StackExchange! Although you are not interested in regression, this is typically a regression problem. Is the original problem more complicated than what you express here? If yes, please provide additional details. $\endgroup$ – Romain Reboulleau Nov 11 '19 at 12:26
  • $\begingroup$ I am new to Machine Learning and I like experimenting. Lately I had the curiosity for what I asked above and could not google it the correct way I guess to find a proper answer. What I was looking for was Ordinal Categorical Classification, given by @serali . $\endgroup$ – E. Vasilopoulos Nov 11 '19 at 19:36
  • $\begingroup$ Cool, I learned something! Feel free to upvote the answer and mark it as accepted. $\endgroup$ – Romain Reboulleau Nov 11 '19 at 20:36
  • $\begingroup$ Ok, I will (not allowed to upvote yet btw). Thank you. $\endgroup$ – E. Vasilopoulos Nov 11 '19 at 23:21
0
$\begingroup$

Correct me if I am wrong but If I understand your question correctly, what you want is a classifier such that classes close to each other (say class 2 and class 3) are preferable to those far away (class 2 and class 99). If this is the case, this problem is called "Ordinal Categorical Classification".

I was working on a similar problem a while ago, and I found this loss function during my research. I ended up not using it so I don't really know how good it works but anyways, hope that helps.

Improve this answer
$\endgroup$
1
  • $\begingroup$ Yes this is exactly the answer I was looking for. I did not know about Ordinal Categorical Classification. Thank you very very much. $\endgroup$ – E. Vasilopoulos Nov 11 '19 at 19:31

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.