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Comparing multiclass performance of scores

I’d like to compare the performance of various classifiers to each other, especially the multiclass ones. My target data consists of close to 50 features, each of which is either in {0,1,2,3} or { -1,0,1,2} range.
Is there a way to do this? Should I convert the features to factors with {0,1,2,3} for each class? I guess the standard way to evaluate classification performance is the confusion matrix. But I’m wondering if there is a better way to evaluate performance, because the results are similar to the confusion matrix, but I don’t want to compare confusion matrices if possible. Any help would be much appreciated.

A:

1) Factorize the set of target classes { -1,0,1,2}. I like using the quadratic term in the chi-square score (from Wikipedia) for each class, i.e. $\chi^2_{quad} = \sum_{i \in A} (O^i – E^i)^2$ with $O^i$ the observed class and $E^i$ the expected class (which is here $i \in \{ -1, 0, 1, 2\}$).
2) Then compare the different classifiers with a metric like e.g. the t-score (again from Wikipedia).
3) Similarly, the (corr

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