A study on rule extraction from several combined neural networks.
Int J Neural Syst
; 11(3): 247-55, 2001 Jun.
Article
en En
| MEDLINE
| ID: mdl-11574962
The problem of rule extraction from neural networks is NP-hard. This work presents a new technique to extract "if-then-else" rules from ensembles of DIMLP neural networks. Rules are extracted in polynomial time with respect to the dimensionality of the problem, the number of examples, and the size of the resulting network. Further, the degree of matching between extracted rules and neural network responses is 100%. Ensembles of DIMLP networks were trained on four data sets in the public domain. Extracted rules were on average significantly more accurate than those extracted from C4.5 decision trees.
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Banco de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Int J Neural Syst
Asunto de la revista:
ENGENHARIA BIOMEDICA
/
INFORMATICA MEDICA
Año:
2001
Tipo del documento:
Article
País de afiliación:
Suiza