Identifying top predictors from a mix of categorical and ordinal data I have a dataset with 261 predictors scraped from a larger set of survey questions. 224 have values which are in a range of scale (some 1-10, some 1-4, some simply binary, all using 0 where no value is given), and the rest are unordered categories.

I'm trying to perform classification using these predictors and identify the top n predictors. Am thinking of the following approach:


convert the 224 ordered predictors into numeric, centered, and scaled.
Run separate modeling (I use caret from R): one for using the numeric predictors, another using the  remaining 37 categorical predictors (both cross-validated within each modeling exercise).
Choose the respective best-fitting models modelN and modelC for the numeric and categorical predictors.
Choose top n (say 10) predictors from model N and model C.
Combine them in an ensemble model that can handle both numeric and categorical data (say, random forest).
Choose top n predictors in the ensemble model.


I am going through this a roundabout way rather than directly fitting all predictors into an ensemble model to try and reduce the complexity of the problem first (and because in R, I'm having a problem with too many levels from the predictors).

Would this be a valid approach to identifying the n most salient predictors? 
Any possible issues to mitigate?
Ricky,

Loose thoughts: 


Depending on the algorithm you intend to use, centering might not be a good idea (e.g. if you go for SVM, centering will destroy sparsity)
I would suggest not to handle ordered / unordered separately, as you are likely to miss interactions that way. If the categorical ones don't have too many possible values, randomForest in R can handle factors.
if that is an issue (as you seem to hint), I think you have two possibilities: binary indicators or response rates
if it's feasible in terms of computational cost, i would convert all factors to binaries (use sparse matrices if necessary) and then try a greedy feature selection. caret, if memory serves, has rfe or somesuch.
if that's too much trouble, try calculating response rates / average values per factor level (I don't see any info whether your problem is classification or regression): you split your set into folds, and then for each fold fit a mixed effects model (e.g. via lme4) on the remainder, using the factor of interest as the main variable. It's a bit of a pain to setup all the cv correctly, but it's the only way to avoid leaking information.


Hope this helps,
K