Artículo
Ameva: An autonomous discretization algorithm
Autor/es | González Abril, Luis
Cuberos, Francisco Javier Velasco Morente, Francisco Ortega Ramírez, Juan Antonio |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos Universidad de Sevilla. Departamento de Economía Aplicada I |
Fecha de publicación | 2009-04 |
Fecha de depósito | 2023-02-14 |
Publicado en |
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Resumen | This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a ... This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a potentially minimal number of discrete intervals. Its most important advantage, in contrast with several existing discretization algorithms, is that it does not need the user to indicate the number of intervals. We have compared Ameva with one of the most relevant discretization algorithms, CAIM. Tests performed comparing these two algorithms show that discrete attributes generated by the Ameva algorithm always have the lowest number of intervals, and even if the number of classes is high, the same computational complexity is maintained. A comparison between the Ameva and the genetic algorithm approaches has been also realized and there are very small differences between these iterative and combinatorial approaches, except when considering the execution time. |
Agencias financiadoras | Ministerio de Educación y Ciencia (MEC). España Junta de Andalucía |
Identificador del proyecto | TSI2006-13390-C02-02
P06-TIC-02141 |
Cita | González Abril, L., Cuberos, F.J., Velasco Morente, F. y Ortega Ramírez, J.A. (2009). Ameva: An autonomous discretization algorithm. Expert Systems with Applications, 36 (3), 5327-5332. https://doi.org/10.1016/j.eswa.2008.06.063. |
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