Compare Classification Performance in Datasets drawn from Different Populations
I understood it wrong ,here is the paper which discuss using multiple data set for the same classifier-

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.141.3142&amp;rep=rep1&amp;type=pdf

They conclude- 
"
We theoretically and empirically analyzed three families of statistical tests that can be used for
comparing two or more classifiers over multiple data sets: parametric tests (the paired t-test and
ANOVA), non-parametric tests (the Wilcoxon and the Friedman test) and the non-parametric test
that assumes no commensurability of the results (sign test). In the theoretical part, we specifically
discussed the possible violations of the tests’ assumptions by a typical machine learning data. Based
on the well known statistical properties of the tests and our knowledge of the machine learning data,
we concluded that the non-parametric tests should be preferred over the parametric ones."


I recently learned about 5x2cv paired t test procedure to compare the performance of two models.

Please refer below-

http://rasbt.github.io/mlxtend/user_guide/evaluate/paired_ttest_5x2cv/

It Implements the 5x2cv paired t test proposed by Dieterrich (1998) to compare the performance of two models.