Random forest vs majority voting I'm using spark with scala to implement majority voting of decision trees and random forest (both are configured in the same way - same depth, the same amount of base classifiers etc.). Dataset is split equally among base classifiers for majority voting. Nemenyi test shows, that majority voting is significantly better (for 11 benchmarking datasets from keel).

From what I understand, the difference between those two methods is that data used to train random forest (base classifiers) might not sum up to the whole dataset. Is my understanding correct? If so, what might be the reason for the observed difference?

Also, could you point me to any articles comparing those two methods?

Edit: If someone was interested in this topic, here's an article comparing bagging with horizontal partitioning in favor of the latter.
Random forest, predicts the class with highest probability estimate. The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.

Majority voting, which is also called Hard Voting, every individual classifier votes for a class, and the majority wins. In statistical terms, the predicted target label of the ensemble is the mode of the distribution of individually predicted labels.

Majority voting may works better in cases where there are some outliers. Consider these votes: $\{0.51, 0.51, 0.51, 0.01\}$ and $\{1,1,1,0\}$.