What is the difference between majority vote, and greedy action in ensembling?
Ensemble learning basically means combining several base models to produce one optimal solution.

And from my understanding,
usually in Majority vote, we train the models with all the data and wait for all the models to result in a prediction and at the end we choose the major result (result that appeared more than any other) as the final output.

But in Greedy way, we usually iterate on the data, run prediction on a subset of the data, pick the best result of this subset and move to the next, and at the end we only consider the best results of each iteration. So we don't look at the big picture here but only a collection of subsets. Not the Global best result but Local best results. 

Hope this helps.