Optimization of fuzzy rule sets using a bacterial evolutionary algorithm
In this paper we present a novel approach where we rst create a large set of (possibly) redundant rules using inductive rule learning and where we use a bacterial evolutionary algorithm to identify the best subset of rules in a subsequent step. This enables us to nd an optimal rule set with respect...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/13175 |
| Acceso en línea: | https://hdl.handle.net/2099/13175 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Intel•ligència artificial Classificació AMS::68 Computer science::68T Artificial intelligence Àrees temàtiques de la UPC::Informàtica::Informàtica teórica |
| Sumario: | In this paper we present a novel approach where we rst create a large set of (possibly) redundant rules using inductive rule learning and where we use a bacterial evolutionary algorithm to identify the best subset of rules in a subsequent step. This enables us to nd an optimal rule set with respect to a freely de nable global goal function, which gives us the possibility to integrate interpretability related quality criteria explicitly in the goal function and to consider the interplay of the overlapping fuzzy rules |
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