This self-contained introduction shows how ensemble methods are used in real-world tasks. It first presents background and terminology for readers unfamiliar with machine learning and pattern recognition. The book then covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, and diversity measures. Moving on to more advanced topics, the author explains details behind ensemble pruning and clustering ensembles. He also describes developments in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.