I am learning Random Forest and have a basic training question. For my problem, I "derived" various classifiers (var0,var1...var9). They are independent, but the intrinsic values from which they are derived overlap. I get the following data for my RF tree. The question I have is, should I eliminate the number of classifiers that haven't shown enough importance (For example, I could scale %IncMSE relatively and may be just pick the top 3 or 4).

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%IncMSE	IncNodePurity
Var0	10.84632	7.232559
var1	24.53021	7.976509
var2	26.5005	4.653162
var3	60.18863	21.882258
var4	11.97568	7.25413
var5	49.63468	16.968472
var6	19.55981	10.009517
var7	10.36669	13.136694
var8	14.16585	7.818673
var9	9.75812	7.178831
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