| Téléchargement | - Voir le manuscrit accepté : Identity verification based on haptic handwritten signature: novel fitness functions for GP framework (PDF, 297 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.1109/HAVE.2013.6679618 |
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| Auteur | Rechercher : Alsulaiman, Fawaz A.; Rechercher : Valdes, Julio J.1; Rechercher : El Saddik, Abdulmotaleb |
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| Affiliation | - Conseil national de recherches du Canada. Technologies de l'information et des communications
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| Format | Texte, Article |
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| Conférence | 2013 IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE 2013), Oct 26-27, 2013, Istanbul, Turkey |
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| Résumé | Fitness functions are the evaluation measures driving evolutionary processes towards solutions. In this paper, three fitness functions are proposed for solving the unbalanced dataset problem in Haptic-based handwritten signatures using genetic programming (GP). The use of these specifically designed fitness functions produced simpler analytical expressions than those obtained with currently available fitness measures, while keeping comparable classification accuracy. The functions introduced in this paper capture explicitly the nature of unbalanced data, exhibit better dimensionality reduction and have better False Rejection Rate. |
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| Date de publication | 2013-11-01 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 21270500 |
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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 | 71ef8fa7-ac7f-4144-b873-1ae61c9c3d5b |
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| Enregistrement créé | 2014-02-14 |
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| Enregistrement modifié | 2020-06-05 |
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