I'm going to implement a feature selection algorithm, and I plan to use the F-score for because of its simplicity. The problem is that, the F-score is used for binary classification. How can it be extended for multi-class classification?
My idea is to use one-vs-all method. That is, if I have
k classes, I would generate
k F-scores for each of these (by taking only one feature at a time and calculate the F-score against all others) and then just take the average. Would this be a good approach?