Random Prototype-based Oracle for Selection-fusion Ensembles
Giuliano Armano, Nima Hatami · 2010
Classifier ensembles based on selection-fusion strategy have recently aroused enormous interest. The main idea underlying this strategy is to use miniensembles instead of monolithic base classifiers in an ensemble in order to improve the overall performance. This paper proposes a classifier selection method to be used in selection-fusion strategies. The method involves first splitting the original classification problem according to some prototypes randomly selected from training data, and then building a classifier on each subset. The trained classifiers, together with an oracle used to switch between them, form a miniensemble of classifier selection. With respect to the other methods used in the selection-fusion framework, the proposed method has proven to be more efficient in the decomposition process with no limitation in the number of resulting partitions. Experimental results on some datasets from the UCI repository show the validity of the proposed method.