That's a really interesting idea! I've done some work with ensemble classifiers that treats different samples with a particular classifier, depending on the confidence level of each, but never at the feature level. Off the top of my head, I think it makes the most since to just do what you're describing at the classification level--classify each sample with both classifiers, and accept the answer of the most-confident one. I'm not sure how it would work at the feature level, as you describe, but I'd be interested to hear your thoughts!
Do you use a standard classification package (e.g., Weka), or have you coded your own pipeline? In my experience, these sorts of outside the box ideas are much easier to do with a system you've coded from the ground up. It gives you a much better grasp of the plumbing connecting the different components of the framework.