dor_id: 45791

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336.#.#.a: Artículo

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351.#.#.b: Journal of Applied Research and Technology

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/144/141

100.1.#.a: Khan, A.; R. Baig, A.

524.#.#.a: Khan, A., et al. (2015). Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm. Journal of Applied Research and Technology; Vol. 13 Núm. 1. Recuperado de https://repositorio.unam.mx/contenidos/45791

245.1.0.a: Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2015

264.#.1.c: 2015-02-01

653.#.#.a: Optimization; genetic algorithm; classification; Feature subset selection

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

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041.#.7.h: eng

520.3.#.a: This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a strong requirement to select a subset of the features before building the classifier. This proposed technique treats feature subset selection as multi-objective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA - II). The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of using NSGA-II for feature subset selection.

773.1.#.t: Journal of Applied Research and Technology; Vol. 13 Núm. 1

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

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doi: https://doi.org/10.1016/S1665-6423(15)30013-4

harvesting_date: 2023-11-08 13:10:00.0

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last_modified: 2024-03-19 14:00:00

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Artículo

Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm

Khan, A.; R. Baig, A.

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Khan, A., et al. (2015). Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm. Journal of Applied Research and Technology; Vol. 13 Núm. 1. Recuperado de https://repositorio.unam.mx/contenidos/45791

Descripción del recurso

Autor(es)
Khan, A.; R. Baig, A.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm
Fecha
2015-02-01
Resumen
This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a strong requirement to select a subset of the features before building the classifier. This proposed technique treats feature subset selection as multi-objective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA - II). The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of using NSGA-II for feature subset selection.
Tema
Optimization; genetic algorithm; classification; Feature subset selection
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

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