Yes. The easiest way to achieve this is to join the two sets of features into a single table (e.g., in an ARFF file). Then use Stacking with two FilteredClassifier objects as the base classifiers: the first FilteredClassifier should use the Remove filter to remove all attributes that are not in your first set of features; the second FilteredClassifier should remove the other features (leaving the class attribute in both sets, obviously).
ClassificationViaRegression with M5P as the base learner has been found (empirically) to be a good *meta* learner in Stacking. You will need to experiment with different base learners in the two FilteredClassifier objects but RandomForest is generally a classifier that gives reasonable results.
Of course, you should compare to building a RandomForest (or whichever base classifier you end up using) on the union of all features to see whether there is any benefit in your multi-view approach based on two separate feature sets.