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Lecture Notes in Computer Science
The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust weights of base classifiers or to select ensemble members. Usually, a weighted sum is used fordoi:10.1007/978-3-642-41822-8_2 fatcat:s2pjq3ekjzdgvfshca6nscadgi