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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1402| LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification | |
| MILTON GARCÍA BORROTO JOSE FRANCISCO MARTINEZ TRINIDAD JESUS ARIEL CARRASCO OCHOA MIGUEL ANGEL MEDINA PEREZ | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Discriminative regularities Emerging patterns Mixed incomplete data Comprehensible classifiers | |
| In this paper, we introduce an efficient algorithm for mining discriminative regularities on databases with mixed and incomplete data. Unlike previous methods, our algorithm does not apply an a priori discretization on numerical features; it extracts regularities from a set of diverse decision trees, induced with a special procedure. Experimental results show that a classifier based on the regularities obtained by our algorithm attains higher classification accuracy, using fewer discriminative regularities than those obtained by previous pattern-based classifiers. Additionally, we show that our classifier is competitive with traditional and state-of-the-art classifiers. | |
| Elsevier Ltd. | |
| 2010 | |
| Artículo | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| García-Borroto, M., et al., (2010). LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification, Pattern Recognition, (43): 3025–3034 | |
| CIENCIA DE LOS ORDENADORES | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | Artículos de Ciencias Computacionales |
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