I have 10 different classes in my classification problem. Each class has about 200 instances, with more than 10.000 features. I performed the classification using Multinomial Bayes classification. However, it turned out that I'm only interested in the results of the 10th category.
My first thought was too merge categories 1 to 9 as negative instances, while making category 10 the positive instances, effectively changing it to a binary problem. However, my advisor tells me that multi-class classification might be better, since there are a lot of features that specifically identify a certain category, while that might not be the case if all instances of category 1 till 9 are thrown together.
I find it hard to fathom that the results will not improve if we change it to a binary classification. Multi-class classification always seems more difficult than a simple binary classification.
What are the general opinions on this? Are there cases were multi-class classification returned better results for a specific class than for using binary classification for that class? I just want to know if this is a possibility in general, so I can defend my choices in the paper. If there are any nice papers/resources about this problem out there, please point towards them!