Artículo
Discovering hierarchical decision rules with evolutive algorithms in supervised learning
Autor/es | Riquelme Santos, José Cristóbal
Aguilar, Jesús S. Toro Bonilla, Miguel |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2000 |
Fecha de depósito | 2020-08-07 |
Publicado en |
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Resumen | This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning
rules in continuous and discrete domains based on evolutive algorithms. The algorithm produces a
hierarchical set of rules, that is, ... This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in continuous and discrete domains based on evolutive algorithms. The algorithm produces a hierarchical set of rules, that is, the rules must be applied in a speciÞc order. With this policy, the number of rules may be reduced because the rules could be one inside of another. The evolutive algorithm uses both real and binary codiÞcation for the individuals of the population and introduces several new genetic operators. In addition, this paper discusses the capability of learning systems based on an evolutive algorithm to reduce both the number of rules and the number of attributes involved in the rule set. We have tested our system on real data from the UCI repository. The results of a 10-fold cross validation are compared to C4.5 s and they show an important improvement. |
Agencias financiadoras | Comisión Interministerial de Ciencia y Tecnología (CICYT). España |
Identificador del proyecto | TIC99-0351 |
Cita | Riquelme Santos, J.C., Aguilar, J.S. y Toro Bonilla, M. (2000). Discovering hierarchical decision rules with evolutive algorithms in supervised learning. The International Journal of Computers, Systems and Signal, 1 (1), 73-84. |
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