Using Accuracy and Diversity to Select Classifiers to Build Ensembles

Ensemble of classifiers is an effective way of improving performance of individual classifiers. However, the task of selecting the ensemble members is often a non-trivial one. For example, in some cases, a bad selection strategy could lead to ensembles with no performance improvement. Thus, many researchers have put a lot of effort in finding an effective method for selecting classifier for building ensembles. In this context, a dynamic classifier selection (DCS) method is proposed, which takes into account both the accuracy and the diversity of the classifiers.

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