ECOC Matrix Pruning Using Accuracy Information
The target of ensemble pruning is to increase efficiency by reducing the ensemble size of a multi classifier system and thus computational and storage costs, without sacrificing and preferably enhancing the generalization performance. However, most state-of-the-art ensemble pruning methods are based on unweighted or weighted voting ensembles; and their extensions to the Error Correcting Output Coding (ECOC) framework is not strongly evident or successful. In this study, a novel strategy for pruning ECOC ensembles which is based on a novel accuracy measure is presented. The measure is defined by establishing the link between the accuracies of the two-class base classifiers in the context of the main multiclass problem. The results show that the method outperforms the ECOC extensions of the state-of-the-art pruning methods in the majority of cases and that it is even possible to improve the generalization performance by only using 30% of the initial ensemble size in certain scenarios.
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