Effects of using reducts in the performance of the irbasir algorithm
Feature selection is a preprocessing technique with the objective of fi nding a subset of attributes that improve the classifi erperformance. In this paper, a new algorithm (IRBASIRRED) is presented for the generation of learning rules that uses feature selection toobtain the knowledge model. Also a...
- Autores:
-
Fernández, Yumilka B.
Bello, Rafael
Filiberto, Yaima
Frías, Mabel
Caballero, Yailé
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2013
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/43790
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/43790
http://bdigital.unal.edu.co/33888/
- Palabra clave:
- Feature selection
classification rules
Particle Swarm Optimization
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Feature selection is a preprocessing technique with the objective of fi nding a subset of attributes that improve the classifi erperformance. In this paper, a new algorithm (IRBASIRRED) is presented for the generation of learning rules that uses feature selection toobtain the knowledge model. Also a new method (REDUCTSIM) is presented for the reduct’s calculation using the optimization technique,Particle Swarm Optimization (PSO). The proposed algorithm was tested on data sets from the UCI Repository and compared with thealgorithms: C4.5, LEM2, MODLEM, EXPLORE and IRBASIR. The results obtained showed that IRBASIRRED is a method that generatesclassifi cation rules using subsets of attributes, obtaining better results than the algorithm where all attributes are used. |
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