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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1842| Classification based on specific rules and inexact coverage | |
| RAUDEL HERNANDEZ LEON Jesús Ariel Carrasco Ochoa José Francisco Martínez Trinidad | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Data mining Supervised classification Class association rules Association rule mining | |
| Association rule mining and classification are important tasks in data mining. Using association rules has proved to be a good approach for classification. In this paper, we propose an accurate classifier based on class association rules (CARs), called CAR-IC, which introduces a new pruning strategy for mining CARs, which allows building specific rules with high confidence. Moreover, we propose and prove three propositions that support the use of a confidence threshold for computing rules that avoids ambiguity at the classification stage. This paper also presents a new way for ordering the set of CARs based on rule size and confidence. Finally, we define a new coverage strategy, which reduces the number of non-covered unseen-transactions during the classification stage. Results over several datasets show that CAR-IC beats the best classifiers based on CARs reported in the literature. | |
| Elsevier Ltd. | |
| 2012 | |
| Artículo | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| Hernández-León, R., et al., (2012). Classification based on specific rules and inexact coverage, Expert Systems with Applications, (39): 11203–11211 | |
| CIENCIA DE LOS ORDENADORES | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | Artículos de Ciencias Computacionales |
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