Why does prediction by a consensus of classifier work better than prediction by a single classifiers?
For a very simple example, imagine you have three independent classifiers that each have 60% accuracy. If you use any one of them to classify a random sample, you have a 60% chance of getting it right.

Now use an ensemble classifier that takes the majority vote. Below are the probabilities that exactly N of the individual classifiers are correct.  

0 correct - 6.4%
1 correct - 28.8%
2 correct - 43.2%
3 correct - 21.6%

Your ensemble classifier has the correct output when 2 or 3 of the component classifiers are correct, which happens 64.8% of the time. Your ensemble classifier outperforms each of the individual component classifiers! You'll get this advantage by combining any classifiers that perform better than random.