Abstract. Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why e n s e m bles can often perform better than any single classi er. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not over t rapidly.
In machine learning, ensemble methods combining multiple learning algorithms usually performs better than any single method REF .
Ensemble Methods in Machine Learning
MULTIPLE CLASSIFIER SYSTEMS, LBCS-1857
Computer Science