Abstract
Ensemble learning is an imperative study in the domain of machine learning. Over the previous years, ensemble learning has drawn considerable attention in the field of artificial intelligence, pattern recognition, machine learning, neural network and data mining. Ensemble learning has shown to be efficient and functional in wide area of problem domain and substantial world application. Ensemble learning, it constructs several classifiers or the set of base learners and merge their output so that the overall variance should be reduced. By merging several classifiers or the set of base learners it significantly improves the accuracy in contrast to single classifier or single base learner. In this literature we survey the various ensemble learning techniques that is prevalent in machine learning.
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