A framework for selection and fusion of pattern classifiers in multimedia recognition
Description and learning techniques are studied in four applications. A new framework for selection and fusion of classifiers is created. A new strategy for selecting classifiers based on diversity measures is shown. Comparable results to well-known techniques from the literature are achieved with fewer classifiers.
diversity measures meta-learning ensemble of classifiers