| Téléchargement | - Voir le manuscrit accepté : Reducing the overconfidence of base classifiers when combining their decisions (PDF, 383 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.1007/3-540-44938-8_7 |
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| Auteur | Rechercher : Raudys, Šarunas; Rechercher : Somorjai, Ray1; Rechercher : Baumgartner, Richard1 |
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| Affiliation | - Conseil national de recherches du Canada. Institut du biodiagnostic du CNRC
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| Format | Texte, Chapitre de livre |
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| Conférence | 4th International Workshop on Multiple Classifier Systems (MCS 2003), June 11-13, 2003, Guildford, United Kingdom |
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| Sujet | multiple classification systems; fusion rule; BKS method; local classifiers; sample size; apparent error; complexity; stacked generalization |
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| Résumé | When the sample size is small, the optimistically biased outputs produced by expert classifiers create serious problems for the combiner rule designer. To overcome these problems, we derive analytical expressions for bias reduction for situations when the standard Gaussian density-based quadratic classifiers serve as experts and the decisions of the base experts are aggregated by the behavior-space-knowledge (BKS) method. These reduction terms diminish the experts’ overconfidence and improve the multiple classification system’s generalization ability. The bias-reduction approach is compared with the standard BKS, majority voting and stacked generalization fusion rules on two real-life datasets for which the different base expert aggregates comprise the multiple classification system. |
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| Date de publication | 2003 |
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| Maison d’édition | Springer Berlin Heidelberg |
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| Dans | |
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| Série | |
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| Publications évaluées par des pairs | Oui |
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| Numéro du CNRC | NRC-IBD-2055 |
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| Numéro NPARC | 9148007 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 8a1144bd-69c8-49fe-9ae2-8ef86f388615 |
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| Enregistrement créé | 2012-10-22 |
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| Enregistrement modifié | 2020-06-12 |
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