2
$\begingroup$

This question already has an answer here:

In my dataset, I have 2 labels, positive and negative. Most samples belong to only one class, either positive or negative. A small fraction of samples take both labels i.e. both positive and negative. Thus, making it a multi-label classification problem. Usually, this could be solved in many different ways including problem transformation or using multi-label classifiers. My question is, can I consider the samples that take both labels as a third class and consider this as a multi-class classification problem with 3 classes?

As far as I understand, this method is not preferred in multi-label classification scenarios where the no. of individual labels is large. That is, if $n$ is the no. individual labels, then a sample could take any of the $2^n-1$ combination of labels. Hence for large $n$, opting for a multi-class classification (with $2^n-1$ classes) could be a bad idea. But, in my case $n=2$, which is a trivial case. Hence, I am considering multi-class classification as an option. Please advise on this and let me know if I am right. Also, if possible please point to literature where such trivial case of multi-label classification problems are dealt with.

$\endgroup$

marked as duplicate by Tom Minka, Andy, gung, kjetil b halvorsen, whuber Dec 3 '14 at 16:16

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.