Researchers from various disciplines such as pattern recognition statistics and machine learning have explored the use of ensemble methodology since the late seventies. Thus they are faced with a wide variety of methods given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods theories trends challenges and applications.The book describes in detail the classical methods as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.