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- W157361875 abstract "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." @default.
- W157361875 created "2016-06-24" @default.
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- W157361875 date "2013-01-01" @default.
- W157361875 modified "2023-09-23" @default.
- W157361875 title "ECOC Matrix Pruning Using Accuracy Information" @default.
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- W157361875 doi "https://doi.org/10.1007/978-3-642-38067-9_34" @default.
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