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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2275| InstancRank based on borders for instance selection | |
| Pablo Francisco Hernández Leal Jesús Ariel Carrasco Ochoa José Francisco Martínez Trinidad José Arturo Olvera López | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Instance selection algorithms are used for reducing the number of training instances. However, most of them suffer from long runtimes which results in the incapability to be used with large datasets. In this work, we introduce an Instance Ranking per class using Borders (instances near to instances belonging to different classes), using this ranking we propose an instance selection algorithm (IRB). We evaluated the proposed algorithm using k-NN with small and large datasets, comparing it against state of the art instance selection algorithms. In our experiments, for large datasets IRB has the best compromise between time and accuracy. We also tested our algorithm using SVM, LWLR and C4.5 classifiers, in all cases the selection computed by our algorithm obtained the best accuracies in average. | |
| Elsevier Ltd. | |
| 2013 | |
| Artículo | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| Hernández-Leal, P., et al., (2013). InstanceRank based on borders for instance selection, Pattern Recognition, (46): 365-375 | |
| CIENCIA DE LOS ORDENADORES | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | Artículos de Ciencias Computacionales |
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| 192. InstanceRank based on borders for instance selection.pdf | 549.95 kB | Adobe PDF | Visualizar/Abrir |