An improvement to the classification based on the measurement of the similarity quality using fuzzy relations
The learning of classification rules is a classic problem of the automatic learning. The algorithm IRBASIR for the induction of classification rules based on similaridad relations allows to discover knowledge starting from decision systems that contain features with continuous and discrete domains....
- Autores:
-
Fernandez Hernandez, Yumilka Barbara
Filiberto, Yaima
Frias, Mabel
Bello, Rafael
Caballero, Yaile
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/60647
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/60647
http://bdigital.unal.edu.co/58979/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
classification rules
fuzzy sets
similarity relations
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | The learning of classification rules is a classic problem of the automatic learning. The algorithm IRBASIR for the induction of classification rules based on similaridad relations allows to discover knowledge starting from decision systems that contain features with continuous and discrete domains. This algorithm has shown to obtain higher results than other well-known algorithms. In this article, several modifications to this algorithm based on the Fuzzy sets theory are proposed, taking into account the measure quality of similarity. The experimental results show that using the fuzzy sets theory allow to obtain higher results than the original algorithm. |
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