I have several confusion tables calculated, see, e.g., table 2 and table 1, but typically I'll have 10 or so. These confusion tables are the result of different classification processes (in fact they are the result of different sensor locations). The row and column labels are the same in each table, only the values and thus the accuracy (the last column) differs.
Now I want to run a test which can tell me which table performs better (i.e, which sensor location is best). I thought of just taking the average of the accuracy, but I would also like to know which rows attribute the most to the difference.