Artículo
Evolutionary Learning of Hierarchical Decision Rules
Autor/es | Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal Toro Bonilla, Miguel |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2003 |
Fecha de depósito | 2016-06-27 |
Publicado en |
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Resumen | This paper describes an approach based on evolutionary
algorithms, hierarchical decision rules (HIDER), for
learning rules in continuous and discrete domains. The algorithm
produces a hierarchical set of rules, that is, ... This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must be, therefore, tried in order until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We have tested our system on real data from the UCI Repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice. |
Cita | Aguilar Ruiz, J.S., Riquelme Santos, J.C. y Toro Bonilla, M. (2003). Evolutionary Learning of Hierarchical Decision Rules. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 23 (2), 324-331. |
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