Editorial of the Special Issue on Hybrid and Ensemble Methods in Machine Learning
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Hybrid and ensemble methods in machine learning have attracted a great attention of the scientific community over the last years [Zhou, 12]. Multiple, ensemble learning models have been theoretically and empirically shown to provide significantly better
performance than single weak learners, especially while dealing with high dimensional, complex regression and classification problems [Brazdil, 09], [Okun, 08]. Adaptive hybrid systems has become essential in computational intelligence and soft computing, as being able to deal with evolving components [Lughofer, 11], non-stationary environments [Sayed-Mouchaweh, 12] and concept drift (as presented in the first paper of this special issue, see below). Another main
reason for their popularity is the high complementary of its components. The integration of the basic technologies into hybrid machine learning solutions [Cios, 02] facilitate more intelligent search and reasoning methods that match various domain
knowledge with empirical data to solve advanced and complex problems [Sun, 00].