I am building a multi-class support vector machine (8 classes to be precise) on an image dataset of pre-defined classes. And then I thought of a question:
What if I have an image that doesn't belong to the set of predefined classes, what would be the outcome?
So I decided to experiment with it and the result was very bad. I got a higher accuracy for images that don't belong to any of the classes. Some images gave 98% accuracy, that they belonged to a particular class, even though my expectation should be that they should should have a very low accuracy.
I also tried using OnceClass SVM to first predict if it's part of the class or not. If yes, then what's the label? (Meaning I have 2 models).
But this doesn't seem to work as the OneClass SVM couldn't classify the "other" images well. Now I am running out of ideas of how to go about it.
How can I approach this problem?