Chi-Squared test: ok for selecting significant features?
I will raise several issues that could arise if you are selecting features based on chi-2 tests

Repeated use of chi-2 test can lead to spurious results unless you correct for the number of times you run it

You can include features that are correlated with each other, i.a. A is correlated with B, and both are correlated with label. Not sure, but I think, this can lead to results where model performs worse with more features.


I would try starting with all the features, remove the ones linearly correlated.  But this is just a suggestion.
Also, mutual information can be used to estimate how well any given feature describes the label.