Q:

Rearranging 6-class model to 6 2-class models. Will it give any improvement?

I have a SVM model consisting of 6 classes and 19 features. It works well, 95% accuracy.
I'm evaluating, how to get the last 5%. My idea is to create other models with other features, train instances.
Another idea is to rearrange the existing model from 6 classes to 6 models each with 2 classes, where one class is positive and the other 5 classes are negative. The features will remain the same. Will it bring any new classification results, or is it just a redundant model?
Thank you!

A:

My idea is to create other models with other features, train
  instances.

Yes, it's a good idea. Check performance of other models on your data. 

Another idea is to rearrange the existing model from 6 classes to 6
  models each with 2 classes, where one class is positive and the other
  5 classes are negative.

Since SVM is a binary classifier. A multiclass SVM classifier internally uses either One-Vs-All or One-vs-One. What you are suggesting is one-vs-all. Since libsvm uses One-vs-One technique. you can use one-vs-all but this usually doesn't increase accuracy performance as one-vs-one uses more number of classifier.  

