I am combining multiple base classifiers for an ensemble classifier. Different sensors, such as an accelerometer, gyroscope and altimeter are classified individually, and their outputs are then fed into an ensemble classifier.
For accelerometer, 12 features are extracted from specific time windows, and Random Forest is used. For the altimeter, only one feature is extracted, so I am wondering which algorithm would be best for this?
I know that Random Forest works better when there are many features so I was thinking of using Naive Bayes, or Logistic Regression, but I cannot find any relevant literature to back this up?