# random forest classification appears dependent on Dependent variable proportions

I'm using Random Forest for classification which gives the following confusion matrix.

0 1 class.error

0 839 24 0.027

1 60 86 0.410

You can notice that the classification error is a lot higher for incorrect classification of 1's (false negatives).

I've noticed - even when applying RF to other problems - that the classification error tends be higher on the basis of the proportions of the dependent variable. For eg:- in the example above, there are 146 cases with DV = 1 as opposed to 863 cases with DV = 0 so the error for classifying a case as 1 is much higher

My question is this: What is the reason that the RF algorithm behaves this way and how can I improve the results to remove what seems to me like a bias.